TW503375B - Method for hyperspectral imagery exploitation and pixel spectral unmixing - Google Patents

Abstract

An efficiently hybrid approach to exploit hyperspectral imagery and unmix spectral pixels. This hybrid approach uses a genetic algorithm to solve the abundance vector for the first pixel of a hyperspectral image cube. This abundance vector is used as initial state in a robust filter to derive the abundance estimate for the next pixel. By using Kalman filter, the abundance estimate for a pixel can be obtained in one iteration procedure which is much fast than genetic algorithm. The output of the robust filter is fed to genetic algorithm again to derive accurate abundance estimate for the current pixel. The using of robust filter solution as starting point of the genetic algorithm speeds up the evolution of the genetic algorithm. After obtaining the accurate abundance estimate, the procedure goes to next pixel, and uses the output of genetic algorithm as the previous state estimate to derive abundance estimate for this pixel using robust filter. And again use the genetic algorithm to derive accurate abundance estimate efficiently based on the robust filter solution. This iteration continues until pixels in a hyperspectral image cube end.

Description

～ 3 ί) ～ 3 ί) Α7 Β7, invention description (high spectrum data, the idiom has its difficulty. The common feature of all _ difficult is that it contains a lot of scary narrow spectrum frequency bands. The exact frequency band of each healthy spectrum image is different. Very large. A single band in the visible wavelength range can be sharpened from 1 nanometer to a few millimeters. 51 The band in the infrared_wavelength range 5 may exceed the band in the visible wavelength range. Of course, the hyperspectral data is extremely Useful because it is easy to identify the object using hyperspectral data and the extremely special characteristics of the observed object. These extremely special characteristics of the object relate to non-four-frequency spectrum bands. This detection and identification is not easy to achieve with traditional methods. The shortcomings related to high-spectrum data are the ability to process huge amounts of information. Specific elements, objects or objects, or components have their specific, spectral characteristics. You can identify the corresponding spectral characteristics by identifying a specific spectral characteristic. The existing methods for processing hyperspectral data include pattern matching techniques. These techniques rely on models and least squares algorithms to identify and isolate elements within hyperspectral data. These pattern matching techniques suffer from their lack of robustness. 15. As a result, Wei Wei's decline in time and space. They also do not include the use of a combination of spectral characteristics to identify the composition of its elements, and calculate the Huge. The results are also significantly reduced due to changes in sensors and the atmosphere. Traditional methods also cannot handle non-linear 1 and increase the database of elements detected in hyperspectral data. These methods are also not effective. ^ -0 Listening computing technology (Ε〇 It is very suitable for non-linear optimization. Evolutionary computing technology is the result of further development in the recently developed advanced computing field: advanced computing fields include algorithms, genetic programming, artificial neural networks, and artificial life. These technologies are very suitable for transplantation to high-level Spectral data and ^ pattern matching. In addition to matching the database_spectrum characteristics and the spectrum extracted from the height_image (please read the precautions on the back before filling this page) Order ------ 线 # -----

83 · 3 · 10,000, Invention Description (2) 10 15 20 Qin In addition to these characteristics, these evolutionary computing techniques also generate some special methods from the recursive search process, which can be used to analyze hyperspectral data. These technologies are “learning”. The original basis of evolutionary algorithms is the environment of virtual objects, each of which represents a method for analyzing a particular aspect of a set of data. For the description of the evolution algorithm, refer to the first figure. Darwin's idea of "survival of the fittest" in the process of genetics and evolution is the cornerstone of the evolutionary algorithm approach used to optimize tasks. Starting from the set of all possible methods to solve a particular problem, the new generation is unique and expected to be better (or more suitable) ) Method will be generated from the random pairing of its parent or original method. Once the parent method is selected and paired, they will exchange parts of their respective methods. The method or chromosome exchange is called mating or reproduction. Two separate The original method is generated from the breeding process, and the parent method will no longer exist. Only its next generation survives the mating process. The next generation method must be able to analyze the same data that its parent can handle. The mutation may appear randomly in The necessary diversity is introduced in each subsequent generation. The sub-method of each subsequent generation will be tested for its processing ability. The user sets a certain index to evaluate the sub-method. Each generation of the method gets a score. According to the user's index These scores are used to indicate the fitness of the methods in the analysis data, or the degree of avoidance < 5 low fitness The degree method does not allow pairing and breeding of offspring, so that they gradually perish and no longer survive. The method with a high fitness score will allow pairing and mating, but they will breed themselves and continue their race. The fittest survives and evolves The method continues to a specific, user-selected algebra. This leads to rapid convergence of optimization methods for analyzing specific data sets. Tasks such as spectral decomposition, target detection and recognition are extremely suitable for this paper. (210X297 mm 83.3.10,000 (please read the precautions on the back before filling out this page) 503375 A7, invention description (3 10 15 1 20 method combined with this type of evolutionary method. Therefore, as a continuation of the above argument, high-spectrum Data is an ideal application object of evolutionary computing technology. After experiencing all allowed algebras, at the end of the process, the method with the best fitness score obtained according to user indicators is the best method for analyzing a given data set. In general, evolutionary algorithms give much better results than traditional methods, because the search process is very selective. The search algorithm searches through the entire set of candidate methods at the same time. The evolutionary algorithm does not have any difficulty to perform complex optimization tasks with multiple constraints. There is no excessive restriction on non-linearity. Non-linearity is often reflected in the atmosphere and data related to hyperspectral image data. Sensor constraints. The index function associated with fitness assessment also reflects these non-linearities and constraints. The second figure reflects the application of evolutionary computing techniques in hyperspectral data. This application includes a process that is related to spectral wavelengths Reliable technology for single pixel features. Evolutionary algorithm technology includes feature top processing and non-parametric search of models based on hyperspectral image data. When applying evolutionary computation techniques, each pixel of a hyperspectral image needs to perform thousands of Tens of thousands of recursive operations. This requirement results in very slow computational strokes. An appropriately sized hyperspectral image requires millions of recursive operations. Therefore, what is needed is a fast processing method for the analysis and accurate decomposition of hyperspectral images. In order to achieve the above purpose, the present invention provides a hybrid method that uses robust filtering technology to perform fast pixel decomposition of hyperspectral images. And the genetic algorithm is used to refine the content estimation of the robust filter. Another object of the present invention is to provide a hybrid method, when robust filtering -5㈣ of this paper ΛΑϋ uses t ® S Home County (CNS) A4 * L ^ (210x 297 / ^ ¾ g3. 3.! 0, 〇〇〇〇 (Please read the notes on the back before filling this page)

、 Explanation of invention (4 A7 B7

At I, this method uses robust filtering techniques. Ding Ping refers to the pixel decomposition of the image, and uses genetic algorithms to obtain accurate content estimates. Another goal of this issue is to provide a hybrid method, which uses 5 Lu, Kalman filters to perform fast pixel decomposition of hyperspectral images, and uses genetic algorithms to refine the content estimation of robust filters. . Another object of the present invention is to provide a hybrid method, which uses a robust Kalman filter to perform pixel decomposition of fast hyperspectral images when the estimated P 十 error of the robust filter is greater than a preset, and uses genetic Algorithm to get 10 accurate content estimates. Graphical illustrations First figure: is a block diagram describing the method of evolutionary computation. Figure 2: A block diagram depicting the spectral decomposition method of evolutionary computation for hyperspectral images. 15 The third figure: is a functional block diagram describing the preferred implementation of the hybrid method. According to the invention, the hybrid method is used for hyperspectral image analysis and pixel decomposition. Figure 4: A functional block diagram depicting a second preferred implementation of the hybrid approach. According to the present invention, the hybrid method is used for hyperspectral image analysis and pixel decomposition. Figure 5: A functional block diagram depicting the implementation of pixel decomposition of a super-spectral image using a robust Kalman filter. Figure 6: A functional block diagram describing the implementation of the fuzzy logic module. The seventh figure: is a functional block diagram, described in the pixel decomposition of the hyperspectral image. -6- This paper size is applicable to the Chinese National Standard (CNS) A4 specification (21〇X: 297 mm) 83. 3.10,000 (Please read the precautions on the back before filling out this page) Line_ 503375 A7 B7 V. Description of the invention (5) 10 15 Realization of genetic algorithm. Explanation of drawing number = 10- hyperspectral image sensor 30- atmospheric compensation module 50- spectrum library. 61-fuzzy logic module 612-fuzzy inference mechanism 614-fuzzy reduction module 63-covariance propagation module 64-optimal gain calculation module 66- state vector update module 68- state vector prediction module Ή- fitness calculation module 73- reproduction module 75- mutation module 77- binary string 20- image registration module 40- feature pre-processing module 60- robust filter 61 Η Modification module 613-Fuzzy rule base 62-Preprocessing module 64-Optimal gain calculation module 65-Covariance update module 67-Measurement residual calculation module 70-Genetic algorithm decomposition module 72-Decoding module 74-Mating module 76- Coding module 80-artificial neural network module (please read the notes on the back before filling this page) marked 'f # calcium%% installed 90- first pixel content estimation module The present invention provides an effective hybrid method to analyze the high spectrum Image and resolution spectrum pixels. This hybrid method uses a genetic algorithm to solve the content vector of the first pixel of the hyperspectral image cube. The content vector is used as the initial value of the Kalman filter, and the content estimation of the next pixel is obtained by using the Kalman filter technology. With the Kalman filter, the pixel content estimation can be obtained in the Kalman filtering process, and the Kalman filtering is much faster than the genetic algorithm. Kalman and Mann filter; the output is then provided to the genetic algorithm to obtain the current image -7- this paper ίΜ 财 关 家 县 (CNS) A4 · (21Qx297 public director) 83. 3. 10,000 503375 A7 B7 V. Description of the invention 6) Accurate content estimation of prime points. The Kalman filter solution as the starting point of the genetic algorithm will accelerate the evolution of the genetic algorithm. After the accurate content estimation is obtained, the next pixel point is taken, and the output of the genetic algorithm is used as the previous state estimation juice, and the content estimation of the current point is obtained by using the Kalman filter. Based on the 5 Kalman solution, the genetic algorithm is used to obtain an accurate content estimate. By analogy, the recursive process proceeds to the last pixel in the hyperspectral image cube. The idea of the Kalman filter is relatively effective. First, suppose a system that can be described by its state vector. In applying hyperspectral image analysis, 10, the state vector is the target member content vector. State vectors cannot be directly observed, but observation data contaminated by noise can be obtained. The observation data ′ is the measured value of the pixel, which is related to the system state by the following measurement equation: zU) = h (xU)) + v (々) 15 The index value 替代 is used to represent the pixel points recorded. In the above equation, the measurement noise representing the variance is a vector function representing the magic noise. This function describes the relationship between pixel measurement and content vector. It is assumed that there are c materials and ^^ !! (spectral band or channel. It is generally assumed that ^ is greater than 20 or equal to 0 to meet discernibility. According to the hyperspectral image's The linear model is represented by a matrix. In this way, a linear measurement equation 逶 Z (female) = Sx〇) + v (々) is obtained. The Kalman filter is a minimum mean square estimator, and has two obvious features: 83. 3.10, 000 (Please read the notes on the back before filling out this page) This paper size is applicable to the Chinese National Standard (CNS) A4 size (210x297 male thin) Invention description (7 10 sign. The first _ sign is Carl «, the wave is based on This feature allows Qiaoyi to treat the button as a-body, rather than as a single-single rhyme component. The record is a recursive form of the card forging ship. = State estimation of the button age machine Calculating the characteristics based on secret factors makes the Kalman filter more effective than the entire input data at each step in the filtering process to calculate the estimated value. Kalman filtering is used to decompose the hyperspectral image. The advantages include: (1) using the content vector between adjacent pixels Relationship to accurately estimate the percentage of material contained in a pixel; (2) processing a pixel with Kalman recursion only once, which leads to efficient analysis of hyperspectral images. In many information extraction applications, according to the laws of physics, The transfer relationship of the state of the internal dynamic system that cannot be directly measured by the sensor is usually described by a non-linear lifetime differential equation. (Please read the precautions on the back before filling this page) IP " Order 4M lacquer 15 x (〇 = f (x (0) + w (t) where x (i) is a state vector, f (x) is a non-linear vector function describing system dynamics, and w (0 is a system with a covariance matrix of Q (〖) Noise. In order to perform Kalman filtering on hyperspectral image decomposition, an equation of state is required. This equation must correlate the content vector of the current pixel with the content vector of the previous 20 pixel. According to the previous equation, this relationship describes As follows: x (k + lh0 (k + L k) x (k) \ w (k) where is the CLC state transition matrix 'and associates the system state at the pixel with the system state at the pixel. Get up. China National Standards (CNS) A4 specification (plus 7 mm of the parent) 83.3.10,000 503375 A7 B7 V. Description of the invention (8) 10 15 $ St ,. 20 jsL · W Tan gn The hybrid method of hyperspectral image decomposition is composed of the following steps: (a) A hyperspectral image cube is received from a hyperspectral image sensor 10. The hyperspectral image cube represents a scene in wavelength and spatial position (unusual). (B) In the image registration module 20, image data is registered on a band-by-band basis. The registration operation is to "convert an image in one band to another band image" without having to involve map coordinate system transformation. This step ensures that the physical position of a pixel corresponding to a band image is consistent with the physical location of a pixel in another band image. (C) Send the registered hyperspectral image cube to the atmospheric compensation module 30 The atmospheric effect is compensated in the atmospheric compensation module 30. In this step, atmospheric correction can be done with the market model FLAASH (Fast Sight Atmospheric Analysis of Spectrum Super Cube). FLAASH is a “Atmospheric Correction” software package based on MODTRAN, developed by AirForcePhii1ips Laboratoiy and SpectraiSciences, Inc. of Hanscom Air Force Base. The software supports current and planned infrared-visible light-ultraviolet hyperspectral and multispectral sensors. The software provides accurate physical derivation of surface and atmospheric characteristics (such as ground π albedo, surface surface degree, water vapor, optical depth of aerosols and clouds, surface and temperature). (D) Receive the spectral characteristics of the material of interest from the spectral library 50, and pre-process the characteristics in the characteristic pre-processing module 4Q. Spectrum library is a database of known materials and objects, and provides its special 83 · 3.10,000 (please read the precautions on the back before filling this page) Order -10- 503375 A7 B7 V. Description of the invention (9 Levy 10 15 , Itchyhoof # 4¾¾¾¾ 20 Some of the features obtained from the laboratory are the reflectance corresponding to the relative wavelength of a single material. The features from the spectral library are orthogonally normalized in the feature pre-processing module 40. This process decomposes the feature space Into a set of orthogonal vector sets. They are linear combinations of known materials or objects. 5 (e) The first pixel content estimation module 90 receives the orthogonal features from the feature pre-processing module 40 and the atmospheric features from the atmospheric compensation module 30. The first pixel is measured and the content vector of the first pixel is calculated. The content estimate of the first pixel will be used as the initial value of the robust filter 60. For pixels other than the first pixel, the first The pixel content estimation module 90 bypasses the measurement data from the atmospheric compensation module 30. There are different algorithms for estimating the content vector of the first pixel. The available algorithms include the minimum Multiplicative (LS) estimator, maximum likelihood (ML) 'and evolutionary algorithm (EA). (F) The robust filter receives orthogonal features from the feature pre-processing module 40 and current pixels from the atmospheric compensation module 30 Point measurement data, and content estimation from the content vector estimation module 90 of the first pixel, and use Kalman filtering technology to perform spectral decomposition of the current pixel. The robust filter 60 outputs the content estimation of the current pixel to the genetic algorithm The decomposition module 70. (g) The genetic algorithm decomposition module 70 receives the orthogonal features of the feature pre-processing module 40, the current pixel measurement value from the atmospheric compensation module 30, and the current pixel content estimation from the robust filter 60. , And use genetic algorithm to perform accurate spectral decomposition of the current pixel to obtain an accurate content estimate. -11- ---------- (Please read the precautions on the back before filling this page)-Order this paper The scale is applicable to the Chinese National Standard (CNS) A4 (210X297 mm) 83 · 3 · 丨 〇, 00〇503503 A7 B7 V. Description of the invention (10) The accurate content estimate is the system output. At the same time, The value is also fed back to the robust filter 60 in the robust filter as a new initial value for the content estimation of the next pixel. In this step, the output of the robust filter 60 is used as a genetic algorithm decomposition 5 module 7. Starting point to accelerate the northbound pass of the genetic algorithm / ·-, (h) move to the next pixel point, and the robust filter 60 receives the orthogonal features from the special feature pre-processing module 40 from the atmospheric compensation The current pixel measurement value of module 30 and the content estimation of the previous pixel from the genetic decomposition module 70, and the Kalman filtering technology is used to perform frequency 10 spectral decomposition on the current pixel. In this step, the genetic algorithm decomposition The accurate content estimation of module 70 is used in the robust filter 60 as the content estimation of the previous pixel in order to accurately estimate the content vector of the current pixel. (i) Return to step (g), and repeat steps (g) and (h) in the hyperspectral characteristic cube until the last pixel. The value of 15 points out that the robust filter 60 and the genetic algorithm can run in parallel. As shown in the fourth figure, after step (d), another step is added, that is, the artificial neural network module 80 is used for material classification selection. This step can reduce the elements of the content vector to accelerate the analysis of hyperspectral characteristics. Therefore, after step (d), the method of the present invention consists of the following steps. (e ') The artificial neural network 80 receives the orthogonal features from the feature pre-processing module 40 and the current pixel measurement data from the atmospheric compensation module 30, and performs material classification selection. The classification data is sent to a genetic algorithm decomposition module 70 and a robust filter 60. The classification data of the first pixel point is also sent to the first image. -12- This paper Caiguan County (CNS) A4 · (21GX297 public director) 83.3.10,000 (Please read the precautions on the back before filling this page) Order 5 Description of the invention (ll A7 B7 J point content purchase 0. _, artificial nerve _ she bypasses the measurement data from the atmospheric compensation module 30 to subsequent modules. ) Is called = class, and is used by human I neural network _ in correction sensor _. The corrected pixel input data from the current compensation module 30 is processed by artificial neural network to record the reading sign and face the known difficulty. The correlation degree of ^. ★ In the fuzzy neural network, the output of all neurons is used to evaluate to determine whether any neuron's response exceeds a preset value, such as · 0 · 5 (output range of neurons: 0 · 0 ~ 1 · 0) β If there are one or more neurons in the neural network whose response exceeds the preset value, the classification corresponding to the neuron is selected = continuation_candidate method. The advantage of classification selection is that it is only related to the current pixel. Related material content is only in Kalman filter and genetic calculation It is used as the element of the state vector in this method. This step can reduce the number of elements of the content vector, 15 to speed up the process. (F ') The first pixel content estimation module 90 receives the JE intersection features from the feature preprocessing module, Compensation module 3_ The first pixel measurement data and the classification data of the first pixel from the artificial neural network 80, and calculate the content vector of the first pixel. The content estimation value of the first pixel is used as The initial value of the robust filter 60. For pixels other than the first pixel, the first pixel content estimation module 90 will only bypass the measurement data from the artificial neural network 80. There are different algorithms available for the first The algorithm of content vector estimation of pixels includes the least squares (LS) economizer and the maximum likelihood method.

(ML I (Please read the notes on the back before filling this page)

1T -13- redundant (applicable to China 2iOX297 public thin) 83.3.10,000,000 503375 A7 B7 V. Description of the invention (12)), and evolutionary algorithm (EA). (g ') The robust filter 60 receives the orthogonal features of the feature pre-processing module 40, the measurement data of the current pixels of the atmospheric compensation module 30, the classification data of the artificial neural network 80, and the first pixel content estimation module. The content of the first pixel of 90 is estimated at 5 pixels, and the Kalman filtering technique is used to perform spectral analysis on the current pixel. The robust filter 60 outputs the content estimation of the current pixel to the genetic algorithm decomposition module 7Q. (h ') The genetic algorithm decomposition module 70 receives the orthogonal features of the feature pre-processing module 40, the measurement data of the current pixels of the atmospheric compensation module 30, the classification data of the artificial neural network 80, and the first pixel content estimation module 9 The content of the first pixel point of 〇 is estimated, and the genetic pixel is used to perform a true spectral decomposition on the current pixel point to obtain an accurate content estimate from TO. This accurate content estimate is the system output. At the same time, the accurate content estimation value is also fed back to the robust filter 60, which is used as a new initial value for the next pixel content estimation in the robust filter. In this step, the output of the robust filter 60 is used as the starting point of the genetic algorithm decomposition module in order to accelerate the evolution of the genetic algorithm. (Γ) moves to the next pixel, the robust filter 60 receives the orthogonal features from the feature pre-processing module 40, the current pixel measurement from the atmospheric compensation module 30, the classification of the artificial neural network 80, and The genetic pixel 20 solution module 70 estimates the content of the previous pixel, and uses Kalman filtering technology to perform spectral decomposition on the current pixel. In this step, the accurate content estimation from the genetic algorithm decomposition module 70 is used in the robust filter go as the content estimation of the previous pixel in order to accurately estimate the content vector of the current pixel. -14- The size of this paper is applicable to China National Standard (CNS) A4 (210X297 mm) 83. 3.10,000 I -------- m— C Please read the precautions for your memory and fill in this 503375 A7 B7 5. Description of the invention (13) ㈠ ') Go back to step (h,), and repeat the recursive steps (h') and (Γ) in the hyperspectral characteristic cube until the final -regret point. (Please read the notes on the back before filling this page} In some applications, the processing speed is the most important, and the content estimation accuracy is secondary. Then, step (g) can be replaced by the following process: 5 Evaluate the current Pixel content content estimation error. If the estimation error is greater than a preset value, step (g) is performed, where the estimation error is given by a robust filter and a covariance matrix. Otherwise, the genetic algorithm decomposition module 70 only bypasses the robustness Solution of filter 60. Similarly, step (h,) can be replaced by the following process: 10 Evaluate the content vector estimation error of the current pixel. If the estimation error is greater than a preset value, then step (h,) is performed, where the estimation error It is given by the covariance matrix of the robust filter. Otherwise, the genetic algorithm decomposition module 70 only bypasses the solution of the robust filter. With reference to the seventh figure, the genetic algorithm decomposition of the genetic algorithm decomposition module 70 is further divided by 15 The following steps are composed: (1) The encoding module 76 randomly generates a set of binary character strings π. This set of unary character strings represents a pixel of a hyperspectral image cube. The quantity vector. The genetic algorithm operates on this set of binary strings instead of the content vector itself. The β binary string is sent to the decoding module 72 20 ° (2) The decoding module 72 decodes this set of binary strings 77. Decode The output of the module is a set of content vectors about the pixels of the super-spectral image cube. This set of content vectors is sent to the fitness calculation model. The content vector gives the percentage of each material of interest contained in the pixel. -15- (CNS) A4 ^ (210X297 ^ *)-~ --- ^ _ 83. 3.10,000 503375 A7 -------- B7 V. Description of Invention (χ4) " ^---- (3) The fitness degree miscellaneous surface 71 calculates the fitness value of the miscellaneous content vector. In the fitness degree calculation module, the index function takes a binary string and returns a value. The unsigned string is also called It is a chromosome. Then, the value of the indicator function is mapped to a fitness to adapt to the genetic algorithm. The fit value is an answer based on the performance of all possible solutions represented by the string. The solution (content) of the string (chromosome) is compiled. Vector) The better, the fitness value High. The fitness value is then sent to the breeding module 73 α (4) In the breeding module 73, breeding is performed based on the fitness output from the fitness calculation module 71 W. Reproduction is based on the law of survival of the fittest. These have high fitness values The strings are reproduced in large numbers in the new generation. Once these strings are propagated or copied for use as the next generation, they will perform two other types of operations in the pairing set, namely mating and mutation, thereby multiplying. (5) In mating In module 74, a set of substrings is formed by exchanging the 15 heads and tails of a string (chromosome). Mating provides a mechanism for binary strings to mix and match their desired qualities through a random process. First, 'two newly generated character strings are selected from the matching set formed by the breeding module 73. Second, randomly select a swap position β along the two character strings. Third, swap all characters after the swap position. Although mating makes ^ 20 randomly selected, this cannot be seen as a random walk in the search space. When combined with the reproduction process, this is an effective means of exchanging information and forming high-quality solutions. • (6) In the mutation module 75, the value at a specific character: string position is occasionally changed. This step enhances the genetic algorithm to find an approximate optimal solution. -16- The scale of this paper uses the Chinese National Standard (CNS) M specification (210X297 mm) 83 · 3.! 〇, 〇〇〇 ^-(please first Read the precautions on the back and fill in this page) Order five. Invention description (a- ~ ----- The ability is only different from the position of the lungs _ long-term loss ─ Wei Wei measures ° The probability of occurrence of mutation is extremely low, A string is mutated in the string set. ^, (7) Send the new binary string set to the decoding module 72, and then press (2), (3), ⑷, ⑸, ⑹, and执行 Step execution ^ In step (3), a screening process is performed to determine whether to terminate the evolution. The screening index is defined as the total evolution algebra. When the genetic algorithm is recursive to the total evolution algebra, a binary character with a large fitness value is selected. The string is used as a solution. The corresponding content is 1: vector 1: is the content of 谕 Sulai content. It can also be identified by evaluating the difference between 10 strings. If the difference between the strings is less than a preset value, exit evolution. Similarly , Select _ binary characters with the largest fitness value As a solution. Because the Kalman filter produces an optimal estimate based on the defined statistical characteristics, the estimate is unbiased and has a minimum of 15 variance in the linear unbiased estimate ^. However, its estimation quality is only based on the assumption that the mathematical model holds. It is guaranteed that any error in the β model may invalidate the filtering result, so 'any conclusion based on this result is also invalid β in the hybrid method of the present invention' Another option for a robust filter for unitary spectral decomposition is Universal robust Kalman filter. The universal robust Kalman filter can run stably under multiple dynamic environments / systems. Referring to the fifth figure, the universal robust filtering method for hyperspectral decomposition consists of the following steps ... ( fl) Receive pixel measurement data from the atmospheric compensation module 30-17- This paper size applies to China National Standard (CNS) Α4 specification (210X297 mm) 8 3 · 3.! 0, 〇〇〇〇 ---- ----—- I (Please read the notes on the back before filling this page) Order the invention description (16 15 20

I (f · 2) In the fuzzy logic module 61, the measurement data is checked by the fuzzy logic inference method. Here, the fuzzy logic inference method determines whether to reject or correct the measurement data or to acknowledge the measurement data according to the rules of the fuzzy logic. (f.3) Output correct measurement data or error flags to the pre-processing module 62, where the pre-processing module 62 performs calculation of the state transition matrix and the measurement matrix. (f · 4) The calculated state transition moment barrier is sent from the preprocessing module 62 to the state vector prediction module 66, and the previous state vector is sent from the state vector update module 68 to the state vector prediction module 66. Here, the state vector The prediction module 66 performs state vector prediction, that is, the content vector of the next pixel. (f. 5) The calculated state transition moment barrier is sent from the preprocessing module to the covariance propagation module 63. Here, the covariance propagation module 63 calculates the covariance of the current estimation error. (f · 6) The measurement matrix and the current measurement vector are sent from the preprocessing module 62 to the measurement residual calculation module 67. Here, the measurement residual calculation module 67 receives the state vector prediction value from the state vector prediction module 66, and The measurement residual is calculated by subtracting the product of the measurement matrix and the predicted value of the state vector from the current measurement vector. (f · 10 sends the covariance of the current estimation error from the covariance propagation module 63 to the optimal gain calculation module 64, where the optimal gain calculation module 64 performs the optimal gain calculation. (f. 8) The optimal gain The optimal gain calculation module 64 is sent to the covariance update module 65, where the covariance update module 64 updates the estimated error * 18- This paper is used as a paper towel; ^ (CNS) ~ Waki (21QX297 public envy> 83. 3.10,000 (Please read the notes on the back before filling this page)-^ 1. Ordering line-503375 A7 B7 V. Description of the invention (17) 10 Covariance. (F · 9) Covariance of estimated error The updated value is sent from the covariance update module 65 to the covariance propagation module 63. (f · 10) The optimal gain is sent from the optimal gain calculation module 64 to the state = quantity update Wei 68, where the state vector update module receives The measurement residual from the measurement residual calculation module 66 is updated and the state vector is updated, that is, the content vector of the next pixel. Referring to the sixth figure, step (f · 2) is further composed of the following steps: ^ (f 2-1) Send the measurement data to the fuzzification module 611, where the fuzzer executes Quantitative mapping, that is, transforming the measurement range to a corresponding domain and blurring it, that is, changing the number data to appropriate semantic values. These semantic values are labeled as fuzzy sets, and the resulting fuzzy inputs are used. The fuzzy set and its membership function (〔〇, 1〕) explain the determined measurement data (please read the notes on the back before filling this page) Order 15 Reading Cui (f · 2-2) Put the fuzzy input into the fuzzer module 611 is sent to the fuzzy inference mechanism 612. Here, the fuzzy inference mechanism 612 essentially imitates human decision-making mechanism and uses fuzzy inference rules to derive fuzzy outputs. The fuzzy logic inference rules from the fuzzy rule base 613 summarize the experts with a set of semantic rules Goals and strategies. The fuzzy rule base consists of application domain knowledge and goals. (L2-3) The fuzzy output is sent from the fuzzy inference mechanism 612 to the fuzzy reduction module 614. Here, the fuzzy reduction module 614 generates a certain Measured data, this data best represents the possible distribution of inferred fuzzy output. -19- This paper scale applies to China National Standards (€ yang) 6 4 ^^ (210 > < 297 PCT) 3. 83. Line 10,000

Claims (1)

503375 A8 B8 C8 D8 Application for patent scope 15 Printed by the Consumer Cooperatives of Intellectual Property Bureau of the Ministry of Economic Affairs-High-spectrum image analysis and pixel spectral decomposition method, composed of the following steps: (a) Receive from-Hyperspectral A hyperspectral image cube like a sensor, the hyperspectral image cube represents a scene by wavelength and spatial position. (B) Register image by band in the -® image registration mode. This image The registration is to convert one band image to another band image without involving Lai Weiwei. The transfer certificate corresponds to the physical position of a pixel in the one band image and the physical position of the pixel in the other band image. The physical locations of the pixels are consistent; t (0) sends the hyperspectral image cube registered in the image registration module to the atmospheric compensation module, and compensates for atmospheric effects in the atmospheric compensation module; and (d) using genetic algorithms and Lu The rod Kalman filtering technology decomposes the South-spectrum image data on a pixel-by-pixel basis until the hyperspectral image cube is completed. 2. "Hyperspectral image analysis and pixel spectrum as described in item 1 of the patent application "Solution method", in step (c), atmospheric correction can be completed with the listed software FLAASH (Fast Sight Atmospheric Analysis of Spectral Supercube), a FLATASH based "MODIFICATION" atmospheric correction "software package from Hanscom Air Force Base Developed by AirForcePhillipsLaboratory and SpectralSciencesJnc., The software supports current and planned infrared-visible-ultraviolet hyperspectral and multispectral sensors. The software provides surface and atmospheric characteristics (such as surface albedo, surface height, water vapor-20-paper) Zhang ^ Degree uses Chinese National Standard (CNS) A ^ i (210 × 297 mm) Please read the precautions on the back before filling in this page. Π Line 503375 A8 B8 C8 D8 Scope of patent application 15 Printed by the Consumer Cooperatives of the Intellectual Property Bureau of the Ministry of Economic Affairs 20 The accurate derivation of vapor, optical depth of aerosols and clouds, surface and temperature). 3. As described in the application of "Special Fiber Circumstances", the "High-Analysis and Pixel Frequency Decomposition Method", in which, step by step-step by step and step by step: (d-1) Receive the The spectral characteristics of the material of interest, and these characteristics are pre-processed in the feature pre-processing module. The spectral features from the frequency spectrum library are orthogonally normalized in the feature pre-processing module, thereby decomposing the feature space into a group Orthogonal feature set; (d 2) The first pixel content estimation module receives a set of first pixel measurement data of the orthogonal special filaments from the feature preprocessing module from the slit compensation module, and calculates the first The content vector of the pixels' pixels other than the first pixel, the first pixel content estimation module only bypasses these measurement data from the atmospheric compensation module; (d-3) the Kalman filter receives from The orthogonal features of the feature preprocessing module, the current pixel measurement data from the atmospheric compensation module, and the content estimation of the first pixel content vector estimation module: and use the Kalman filtering technique Perform spectral decomposition of the current pixel, the content estimate of the first pixel is used as the initial value of the Kalman filter, and the Kalman waver outputs the content estimate of the current pixel; (d ~ 4) the retransmission algorithm The decomposition module receives the orthogonal features of the feature preprocessing module, the current pixel measurement value from the atmospheric compensation module, and the current pixel content estimation from the Kalman filter, and uses the genetic algorithm to the current pixel Points to perform accurate spectral decomposition to obtain accurate content estimates; (Please read the notes on the back before filling in this-gutter · -21- Scope of patent application 10 15 Printed by the Intellectual Property Bureau of the Ministry of Economic Affairs's Consumer Cooperatives 20 (Heart 5) Moved to the next pixel, the Kalman filter receives the orthogonal feature of the = feature module, from the atmospheric compensation module : Then the pixel outlines are received from the previous image of the pass-by block: the combined I estimation of the points, and the current pixel point is spectrally decomposed by using Kalman filtering technology. 1 As described in item 3 of the scope of X's patent application, "Hyperspectral image analysis and pixel frequency solution method", where in step ⑷) _, the content of this section is estimated to be inactive, and the current pixel is accurate The content estimate is fed back to the Kalman waver. In the Kalman filter, the initial value of the content estimation of the next pixel is used. The output of the Kalman filter is decomposed by the starting point of the genetic algorithm module. In order to speed up the genetic algorithm's application of the "Hyperspectral Image Analysis and Pixel Frequency j Decomposition Method" described in item 4 of the patent scope, in step (d-5), the genetic algorithm decomposes the accurate content of the handle block. The estimation is used as the content estimation of the previous pixel point in the Kaqi Tow to accurately estimate the content vector of the current pixel point. The "Hyperspectral image analysis and pixel frequency decomposition method" described in item 5 of the patent application scope, wherein in step (d-2), the parameter estimator for calculating the content estimation of the first pixel is A least squares (LS) estimator. ^ The "Hyperspectral image analysis and pixel point frequency% knife solution method" as described in item 5 of the scope of patent application, wherein in step (d-2), parameters for estimating the content of the first pixel point are calculated The estimator is a Maximum Likelihood (ML) Estimator. -22- This paper drama China --------------- ^ ηϋτ. (Please read the notes on the back before filling this page), aT line 1. ..... 1 I ----- I »* --- 1 ·--II · VI. Application scope 5 10 15 8 8 8 8 ABCD Printed by the Consumer Cooperatives of the Intellectual Property Bureau of the Ministry of Economic Affairs 20 S · For example, the "level eight iridium and ten soil ° image analysis and pixel point resolution method" described in item 5 of the application, wherein in step ii), the parameters of the content estimation of this point are considered. Silk Dragon is an evolutionary operator. i 9_As shown in item 丨 of the scope of patent application ^ Please decompose the method ", the material conversion and pixel system = 1) touch the spectral station characteristics of the material of interest from the touch library. The spectrum ship performs the front-line conversion in Lai Zheng's demissions. ^ The feature space is decomposed into a set of orthogonal feature sets. 1) The human I nerve · touches the ^ positive father feature from the feature affinity module and comes from the atmosphere. Compensation module—set the first pixel black measurement data and select it. The person breaks through the network to bypass the measurement data that pulls the atmospheric compensation module to subsequent modules; (do) the first pixel content estimation module receives The orthogonal ships from the feature pre-processing module, the first-level first-pixel measurement data from the atmospheric compensation module, and the classification data of the first pixel from the artificial neural network are calculated in a single parameter. The first pixel ^ content vector is used for the miscellaneous pixel content content. As the initial value of the Dianman filter, the pixel points other than the first pixel point, the first pixel point contains 2: the estimation module only bypasses From the artificial neural network model This is also measured data of the block; ~ (孓 4) The Kalman filter receives some orthogonal features from the feature pre-processing module, and the current pixel measurement from the atmospheric compensation module -23- This paper is again applicable t 国 国 # 准 （CNS） A4 specification (21〇 × 297 mm) (Please read the precautions on the back before filling out this page}: 、-* &amp; Scope of patent application 5 15 Printed by the Intellectual Property Bureau of the Ministry of Economic Affairs, Consumer Cooperatives, 20 Classification data of neural networks, and the estimation of the first-pixel content, and the content of the first pixel. Technology pixels: ^ :: spectrum decomposition. The waver turns out the visit of the current orthogonal contact scale processing module, and obtains the measurement value of the current pixel from the big louse repairer module, the data from the network, and the Kalman filter. Content estimation, and use the genetic algorithm to decompose the current spectrum accurately to determine the content estimation. The Kalman II = itB is used as a compilation algorithm to resolve the shortcomings of the algorithm in order to accelerate the content estimation.遽 Wave 2 is used as the new initial value of the next __ pixel points; 下) Move to the next __ pixel point, the card receiver receives the difference of the j-characteristics module from the compensation module of the domain. Tianyue i pixel point measurement 1 value comes from the classification data of the artificial neural network, and from the decomposition method of the traditional method to estimate the content, and the current pixel is spectrally decomposed by using the Carr fork filter technology. The accurate content estimation of the genetic abandonment decomposition module is used in the Kalman filter _ used as the previous pixel _ frequency meter to determine the content vector of the current point; (d-7) Return to step (μ) , Repeat the steps (d-5) and (d-6) up to the last pixel in the hyperspectral characteristic cube. 10 · As described in item 9 of the scope of the patent application, "Analysis of the hyperspectral map and pixels --------- Xiang (please read the notes on the back before filling in this # 1. Page)} -2 'This paper uses China National Standards (CNS) (210x297 mm) A8 B8 C8 D8 patent application frequency ° knife solution method ", where in step (d-3), the first pixel is calculated The parameter estimator for the content estimation of points is a least squares (LS) estimator. 11. "Hyperspectral image analysis and pixel frequency solution method" as described in the patent application_item 9, where in Turn to ⑷), and the parameter estimator for estimating the leaf in s of the first pixel is a maximum likelihood (g) estimator. 12: According to the "Hyperspectral Image Analysis and Pixel Frequency Decomposition Method 4" described in item 9 of the scope of the patent application, its order is in step ⑷) +, and the parameter estimator for calculating the content estimation of the first pixel is- Evolutionary algorithm. 13f filed a patent_ item "High" Image Analysis and Pixel Point Frequency Resolution Method ", where step (d) is composed of the following steps: 15 Printed by the Consumers' Cooperative of the Intellectual Property Bureau of the Ministry of Economic Affairs 20, ( d 1) receiving the spectral characteristics of the material of interest from the spectral library, and sub-characteristics, the frequency features from the crane spectral library are orthogonally normalized in the feature preprocessing module, Thus, the feature space is decomposed into a set of orthogonal feature sets; (d 2) the first pixel point content estimation module receives the orthogonal features from the feature preprocessing chess block 'and a first-level first pixel from the atmospheric compensation module Point measurement data, and use a parameter estimator to calculate the content estimate of the first pixel point, except for the first pixel point, the first pixel point contains the material, and the compensation is calculated from the money compensation model. Some measurement data; (d 3) the Karl Cry / Wave filter successively obtained these positive signs from the feature pre-processing module, from the pixel measurement in the domain before complementing the pixels. CNS) A4 (21Qx297 J / J patent application range 15 A8 B8 C8 D8 20 data printed by the Consumer Cooperatives of the Central Standards Bureau of the Ministry of Economic Affairs, and the content estimation of the content vector estimation module of the first pixel, and the current pixel frequency spectrum using Kalman filtering technology Decomposition, the content estimate of the first pixel is used as the initial value of the Kalman filter, and the Kalman filter is used to output the content estimate of the current pixel to the decomposing algorithm decomposition module; &quot; &quot; (d-4) Evaluate the estimation error of the content estimation value of the current pixel, &amp; when the estimation error is greater than a preset value, go to step (Heart 5), where the telephone error is given by the covariance matrix of the Kalman filter, otherwise, Go to step (d-6), the genetic anomaly decomposition module only bypasses the content estimator obtained by the Kalman filter; (d-5) the genetic anomaly decomposition module receives the orthogonal features of the feature pre-processing module , The current pixel measurement value from the atmospheric compensation module, and the current pixel content estimation from the Kalman filter, and using the genetic algorithm to the current image The points are subjected to accurate spectral decomposition to obtain the f-quasi-peak content estimate, which is the system output. The accurate content estimate of the current prime point is fed back to the Kalman filter, which is used as the next pixel point in the Kalman filter. The new initial value of the content estimate. The output of the Kalman filter is used as the starting point of the decomposition module of the genetic algorithm to accelerate the evolution of the genetic algorithm. (Heart 6) Move to the next pixel and the Kalman filter receives = Feature listens to the orthogonal noise of the block, the current pixel jf: value from the donation compensation module, and the previous pixel from the genetic algorithm's block contains the f data, and the current pixel Point for spectral decomposition, the exact content from the genetic algorithm decomposition module is like Manga --------- (Please read the precautions on the back before filling this page) Line-26- Bing Zhang u for China Gu family 2 敝 297 male thin). The estimation is used as the first_pixel-off content estimate in the Kalman filter in order to accurately estimate the content vector of the current point; and (d-7) returns to step (d-4) in the hyperspectral characteristic Repeat the steps (d-4), (d-5) and (d-6) up to the last pixel in the cube. 5 U · As described in the "Method for High-resolution Image Analysis and Pixel Spectrum Decomposition" described in item 13 of the scope of the patent application, in step ii), the parameter estimator for calculating the content estimation of the first pixel is a minimum The squarer (LS) estimator. 15. The "hyperspectral image analysis and pixel 10 spectrum decomposition method" as described in item 13 of the scope of patent application, wherein in step (d-2), a parameter estimator for estimating the content of the first pixel Is a maximum likelihood (ML) estimator. 16. The "Hyperspectral Image Analysis and Pixel Spectrum Decomposition Method" as described in item 13 of the scope of patent application, wherein in step (d-2), the parameter estimation of the content estimation of the 15 prime points of the first image is calculated Is an evolutionary algorithmist. 17. The "Hyperspectral Image Analysis and Pixel Spectrum Decomposition Method" as described in item 1 of the scope of patent application, wherein step (d) consists of the following steps: Printed by the Consumer Cooperatives of the Central Bureau of Standards of the Ministry of Economic Affairs (please First read the notes on the back and then fill out this page), 1T (Heart 1) one after another the spectral features of the material of interest from the spectral library, and pre-process these features in the feature pre-processing module, 20 of these from the spectral library The spectral features are orthogonally normalized in the feature pre-processing module to decompose the feature space into a set of orthogonal feature sets; (heart 2) the artificial neural network receives these orthogonal features from the feature pre-processing module and A set of first pixel measurement data of the atmospheric compensation model soul, and material classification selection, the artificial neural network bypasses from -27- this paper New Zealand financial system 1 (eNS} Α4_ (2ι () χ2970 &gt; Printed by the Consumer Cooperatives of the Central Bureau of Standards of the Ministry of Economic Affairs 503375 A8 B8 C8 ____08____ VI. Application Phases — These measurement data from the atmospheric compensation module to subsequent modules; jd-3 ) The first pixel content estimation module receives the hetero-orthogonal feature from the feature pre-processing module 2, the first pixel measurement data from the Qi donation block, and the 5 from the first pixel of the artificial neural network. , Class, data, and use a parameter estimator to calculate the 1-pixel-containing vector, the content estimate of the first pixel is used as the Kalman filter, the initial value, except for the first pixel Pixels, the first pixel containing the S estimation module only bypasses these measurement data from the artificial neural network module; 10 (° ^ 4) the Kalman filter receives k orthogonalities from the feature preprocessing module Characteristics, the current pixel measurement data from the atmospheric compensation module, the classification data from the artificial neural network, and the content estimation of the first pixel vector estimation module, and the spectrum of the current pixel using Kalman filtering technology Decomposed, the Kalman filter outputs the content estimate of the current 15 pixels to the genetic algorithm decomposition module; one (heart 5) evaluates the content estimate of the current pixel When the estimated error is greater than a preset value, go to step (d-6), where the estimated error is given by the covariance matrix of the Kalman filter, otherwise, go to step (water 7), the report The algorithm decomposition module only bypasses the content estimation by the Kalman filter 20; (d &lt;) The genetic algorithm decomposition module receives the orthogonal features of the feature pre-processing module 'the current pixel measurement from the atmospheric compensation module Value, the classification data from the artificial neural network, and the current pixel content estimate from the Kalman filter, and use the genetic algorithm to the current -28- ----------- Φ! (Please read the notes on the back before filling this page) 、 1T Declared patent scope 10 15 Printed 20 pixels by the Consumer Cooperative of the Central Standards Bureau of the Ministry of Economic Affairs for accurate spectral decomposition to obtain accurate content estimates. The output of the Kalman filter is used as the starting point of the genetic algorithm decomposition module to speed up the process. The evolution of the algorithm, the accurate content estimate is fed back to the Kalman filter ', in the Kalman filter, it is used as the new initial value for the content estimate of the next pixel; 乂 d-7) move to the next pixel, The Kalman filter receives orthogonal features from the feature pre-processing module, current pixel measurement values from the atmospheric compensation module, classification data from the artificial neural network, and previous pixel points from the genetic algorithm decomposition module The content estimation of the current pixel is performed using Kalman filtering technology, and the current pixel is spectrally decomposed. The accurate content estimation from the genetic algorithm decomposition module is used as the content estimation of the previous pixel in the Kalman filter in order to accurately estimate the Content vector at the current point; and (d-8) returns to step (d_5), where the high-spectrum characteristic stands Repeat in vivo recursive step (heart 5) and (d-6) until the last pixel person. 18 = As described in the "Method of Hyperspectral Image Analysis and Pixel Spectrum Decomposition" described in item 17 of the scope of the patent application, where in step ii), the parameter estimator for calculating the content estimation of the -pixel point is a least square Multiplier (LS) estimator. 19. As in the "spectrum analysis and pixel frequency decomposition method" of item 17 of the scope of patent application, in step ⑷) +, the parameter estimator for calculating the content estimation of the first pixel is a very similar (ML) Estimator. 20 · As stated in item 17 of the scope of the patent application, “Hyperspectral Image Analysis and Pixels—Packing · 1 (Please read the precautions on the back before filling out this page), 11 thread paper U applies Chinese national standard- 29- 503375 ABCD printed by the Consumers' Cooperative of the China Standards Bureau of the Ministry of Economic Affairs. 6. The spectrum decomposition method of patent application scope. "In step (d-3), the parameter estimator for calculating the content estimate of the first pixel is An evolutionary algorithmist. 21. The "Hyperspectral Image Analysis and Pixel Spectrum Decomposition Method" as described in item 3 of the scope of the patent application, wherein the Kalman filter is a universal and robust Kalman waver. 22. The "Hyperspectral Image Analysis and Pixel Spectrum Decomposition Method" as described in item 21 of the scope of patent application, wherein the universal robust filter consists of the following steps: (fl) receiving the pixel from the atmospheric compensation module 10 (f · 2) In the fuzzy logic module, use fuzzy logic inference to check the measurement data. Here, the fuzzy logic inference method selectively decides to reject the measurement data based on the rich fuzzy logic rules. Or correct the measurement data, or acknowledge the measurement data; (f.3) output the corrected measurement data or error flags to the pre-processing module 15, where the pre-processing module performs the calculation of the state transition matrix and the measurement moment barrier (F.4) The state transition matrix is sent from the preprocessing module to the state vector prediction module, and the previous state vector is sent from the state vector update module to the state vector prediction module. Here, the state vector prediction module performs State 20 state vector prediction, that is, the content estimation of the next pixel; (f.5) The state transition matrix is sent from the preprocessing module to the covariance Broadcast module, here, the covariance propagation module calculates the covariance of the current estimated error; (16) the measurement moment and the current measurement vector from the preprocessing module -30- this paper; Gu Zhongguan Jiaxian (CNS) (210X297 public envy)----------- Φ -------- 1T ------ ^ φί (Please read the precautions on the back before filling this page) 503375 8 8 88 ABCD Six consumer economic cooperatives of the Central Standards Bureau of the Ministry of Economic Affairs printed the patent application scope and sent it to the measurement residual calculation module. Here, the measurement residual calculation module receives the state vector prediction value from the state vector prediction module and passes Subtract the product of the measurement matrix and the predicted value of the state vector from the current measurement vector to calculate the measurement residual; 5 (f.7) send the covariance of the current estimation error from the covariance propagation module to the optimal gain A calculation module, where the optimal gain calculation module calculates the optimal gain; (f.8) sending the optimal gain from the optimal gain calculation module to the covariance update module, where the covariance update module updates 10 covariance of the estimated error; (f.9) The covariance update value of the estimated error is sent from the covariance update module to the covariance propagation module; and (〇〇) the optimal gain is sent from the optimal gain calculation module to the state vector update module, where the state The vector update module receives the measurement residual from the measurement residual calculation module and performs a state vector update, that is, the content estimator of the next pixel. 23. The "Hyperspectral Image Analysis and Pixel Spectrum Decomposition Method" as described in item 22 of the scope of the patent application, wherein step (f.2) consists of the following steps: (f.2-1) send the measurement data To a fuzzer module, here, the 20 fuzzer performs scalar mapping, that is, the range of the measurement data is converted to a corresponding universe, and fuzzification is performed, that is, the measurement data is transformed into an appropriate semantic value These semantic values are labeled as fuzzy sets, and the fuzzy inputs and their membership functions ([0 ,!]) are used to interpret the determined measurement data for the fuzzy inputs obtained; -31- This paper scale applies Chinese National Standards (CNS) A4 specifications (210X297 mm) —-------------, 玎 ------ (Please read the precautions on the back before filling out this page} 503375 Staff Consumption of Central Standards Bureau, Ministry of Economic Affairs Cooperative prints A8 B8 C8 D8 6. The scope of patent application (f-2-2) sends the fuzzy input from the stenter model to a fuzzy inference mechanism. Here, the right inference mechanism mimics the human Decision mechanism and use fuzzy reasoning rules to derive fuzzy input The fuzzy logic reasoning rule from the fuzzy rule base summarizes the 5 goals and strategies of the expert by means of a set of semantic rules. The fuzzy rule base consists of application domain knowledge and goals; and (£ 2-3) the fuzzy output From the fuzzy reasoning mechanism to a fuzzy reduction module, the fuzzy reduction model generates a valid and broken measurement data, which best expresses the possible distribution of the inferred fuzzy output of 10. 24. If the scope of patent application The "Hyperspectral Image Analysis and Pixel Frequency 4 Decomposition Method" described in item 5, wherein the genetic algorithm decomposition consists of the following steps: (1) A compilation module is randomly generated and a set of binary strings, which The set of binary strings represents the content estimate related to the 15 points of the pixel of the hyperspectral image cube, the genetic algorithm operates on the set of binary characters 亊, and the set of binary characters _ is sent to a decoding module; (2) The decoding module decodes the set of binary strings, and the output of the solution module is about the content of the pixels of the super-spectrum image cube. This set of content estimation set is sent to a 20-meter fitness module. The content estimation gives the percentage of each material of interest contained in the pixel; (3) The fitness calculation module calculates each The fitness value of the content estimate. In the fitness calculation module, the indicator function takes the binary string (also called chromosome) and returns a value, and then the value of the indicator function is -32- this paper scale逋 Use Chinese National Standard (CNS) A4 specification (210X297 mm) ---------- Φ, ------ 1T ------ M. # — (Please read the back first (Please note this page before filling out this page) A8 B8 C8 ------------- D8 Patent Application Scope The Central Consumers Bureau of the Ministry of Economic Affairs's Consumer Cooperatives printed and adapted the repatriation algorithm. The fitness value is based on The answer to the performance of all possible solutions represented by the word 'The Ml # of the coded string is the higher the fitness value, and the fitness value is sent to a breeding module; 5 (4) Perform a discrimination process to determine whether Stop evolution. Here, it is judged that the standard meaning is the total evolution algebra. When the algorithm is recursive, When the total evolution algebra '_ ## is a binary string with the largest fitness value as the solution and the retransmission algorithm exits evolution, its corresponding content estimation vector will be the content estimation of the pixel; 10 (5) in In the breeding module, based on the fitness tB from the fitness calculation module, enter the i, new, and fresh, based on the law of survival of the fittest, that is, there will be a large number of binary strings with high fitness values in the new generation. Copy, once these binary strings are reproduced or copied for use as the next generation, the binary character _ will perform another 15 two types of operations in the pairing set, namely, mating and mutation, thereby multiplying; (6) In the mating module, by swapping The head and tail of the binary string (chromosome) form a set of substrings, and mating provides a mechanism for the binary string to mix and match its desired qualities through a random process. In this random process, first, from the traditional Two newly generated strings are selected from the set of matching 20 formed by the thin module; secondly, one is randomly selected along the two strings consistently Third, exchange all characters after the exchange position; (7) In the mutation module, occasionally change the value at a specific string position, the mutation is a insurance paper against the permanent loss of any simple bit Zhang scale is applicable to China National Standard (CNS) A4 specification (210X 297 mm) (Please read the precautions on the back before filling out this page). Applicable scope of patent for T-line 15 Printed by the Consumers' Cooperative of the Central Standards Bureau of the Ministry of Economic Affairs 20 measures, the probability of occurrence of the mutation is extremely low, and only one character string is mutated; and the system of “combination system” 5 tt ^ () '(4)' (5) '(6), (7 ), And who performed the step. LUt · Email 24 items of "Guo Gu_ 彳 and pixel °° knife method J 'where the genetic algorithm decomposition consists of the following steps: _ (1) An encoding module randomly generates a set of binary string sets, = level The ^ 1 estimation of the binary phase of the pixel-phase phase P of the face-to-face crane crane cube is 3, and the genetic algorithm operates on the set of binary strings, and the set of binary strings is sent to a decoding module;, ( 2) The decoding module decodes this set of binary strings, and the depletion module is about the super-spectrum county pixels, including reading t, and the content estimation set is calculated based on the fitness. Module, the content estimate gives the percentage of each material of interest contained in the pixel; (3) the fitness calculation module calculates the fitness value of each content estimate, and in the fitness calculation module t, the index The function takes the binary character thing (also called chromosome), and returns a value, and then casts the value of the indicator function into a fitness to adapt to the genetic algorithm. The fitness value is based on the sub-symbol string. All The answer to the performance of the possible solution, the better the content of the encoded string is estimated, the higher the fitness value is, and the fitness value is sent to a breeding module person; (4) the judgment process is performed to determine whether to terminate the evolution. Evaluate the difference between the unsigned strings to make a judgment. When the difference between the strings is an average of 1 · 1 in the set of strings (please read the precautions on the back before filling this page). Printed by the Bureau of Standards and Industry, Cooperated with Shellfish Consumer Cooperative 503375 A8 B8 C8 丨 丨 .................... .. -... ~ __ D8 VI. Patent Application Scope-'^: When it is less than-a preset value, the genetic algorithm exits evolution. After that, choose a binary string with the highest fitness value as the solution. The corresponding total amount estimation vector is the content estimate of the pixel; (5) In the breeding module, based on the fitness from the fitness calculation module 5, the rotation is performed and breeding is performed. Here, the dragon is based on the survival of the fittest The rule is that these binary strings with high fitness values will There are a large number of copies. Once these binary strings are reproduced or copied for use as the next generation, the binary performs two other types of operations in the paired set, namely mating and mutation, thereby multiplying; 10 (6) In the mating module, by exchanging the The head and tail of a binary string (chromosome) form a set of substrings. Mating provides a mechanism for the binary string to mix and match its desired qualities through a random process. At this random process t, first, from the breeding module Two newly generated strings are selected from the formed matching set; secondly, a swap position is consistently and randomly selected along the two characters $ 15; third, all characters after the swap position are swapped; (?) In mutation The module orders, occasionally changing the value at a specific string position. This mutation is a insurance measure against the permanent loss of any simple bit. The probability of this mutation is extremely low, so that there is only one string in the set of strings. Mutated; and (8) send the new set of binary strings to the decoding module, and then press (2) (3), (4), (5), (6), (7), and by ⑻ steps. 26. The "Hyperspectral Image Analysis and Pixel Spectrum Decomposition Method" as described in item 25 of the scope of the patent application, wherein step (f.2) consists of the following steps: -35- This paper scale applies Chinese national standards ( CNS) A4 specification (210X297 mm) (Please read the precautions on the back before filling out this page} 、 11 line —____________ 08503375 A8 B8 C8 6. Application for patents Printed by the Consumer Cooperative of the Central Standards Bureau of the Ministry of Economic Affairs (£ 2- 1) The measurement data is sent to a fuzzer module, where the fuzzer performs scalar mapping, that is, the range of _ measurement data is converted to a corresponding domain, and fuzzification is performed, that is, her measurement data transformation Into appropriate semantic values. These semantic values are labeled as fuzzy sets, and the measured data obtained from the fuzzy set and its membership function ([oj]) are used to input the model data obtained from 5; '(f.2-2) The fuzzy input is sent from the fuzzer module to a fuzzy inference mechanism. Here, the fuzzy inference mechanism essentially imitates human decision-making mechanism, and uses fuzzy inference rules to derive fuzzy output from the fuzzy rule = 10 The fuzzy logic reasoning rules of the library summarize the experts' goals and strategies with the help of a set of semantic rules. The fuzzy rule base consists of application domain knowledge and goals; and (f.2-3) sends the fuzzy output from the fuzzy reasoning mechanism. To a fuzzy reduction module, here, the Juju reduction module generates a determined effective measurement of 15 data, which best represents the possible distribution of the inferred fuzzy output. 27. As described in item 9 of the scope of patent application "Hyperspectral t analysis and pixel spectral decomposition method", in which the Kalman filter is a universal robust wave filter. ~ 20 28 · "Hyperspectral image analysis" as described in item 27 of the scope of patent application And pixel point frequency 4-knife solution method ", which claims that the universal robust filter consists of the following steps: (Π) receiving the measurement data of the pixel point from the atmospheric compensation module; (£ 2) in the fuzzy logic module In the test, I use the logic reasoning method of the mold rim to check I., — (Please read the precautions on the back before filling in this I), -0 line -36- This paper size applies to China National Standards (CNS) A4 specifications (210X297) %) 503375 A8 B8 C8 D8 Patent application scope 15 The Consumer Cooperative of the Central Standards Bureau of the Ministry of Economic Affairs has printed and tested the measurement. Here, the ___________________________________ Or acknowledge the measurement data; (〇) output the corrected measurement data or error flag to the preprocessing module, where the preprocessing module performs the calculation of the state transition matrix and the measurement matrix; (£ 4) the state transition matrix The preprocessing module is sent to the state vector prediction module, and the previous state vector is updated from the state vector update module to the state vector prediction module. Here, the state vector prediction module performs state vector prediction, that is, the next pixel point. Content estimation; (f.5) sending the state transition matrix from the preprocessing module to the covariance propagation module, where the covariance propagation module calculates the covariance of the current estimation error; (f.6) the measurement matrix And when the measurement vector is sent from the preprocessing module to the measurement residual calculation module, the measurement residual calculation module receives the prediction vector from the state vector. The state vector predicted value, and calculate the measurement residual by subtracting the product of the measurement matrix and the state vector predicted value from the current measurement vector; (f.7) the covariance of the current estimation error from the The variance propagation module is sent to the optimal gain calculation module, where the optimal gain calculation module calculates the optimal gain; (£ 8) The optimal gain is sent from the optimal gain calculation module to the covariance update module, here , The covariance update module updates the covariance of the estimated error; -37- This paper size applies to China National Standard (CNS) A4 (210 X 297 mm) (Please read the precautions on the back before filling this page )-Gutter Printed by the Consumer Cooperative of the Central Standards Bureau of the Ministry of Economic Affairs ^ (f · 9) The covariance update value of the estimated error is sent from the covariance update module to the covariance propagation module; and the optimal gain is calculated from the optimal gain The module is sent to the state vector update module. Here, the state vector update module receives the measurement residual from the 5 residual measurement difference module, and updates the state deviation amount, that is, the next- Pixel estimator. ^ 29. As described in the application for the "High Touch Image Analysis and Pixel Spectrum Decomposition Method" described in item 28, step (f.2) consists of the following steps: (f.2-1) the measurement data Send it to a fuzzer module, where 10 the fuzzer performs scalar mapping, that is, the scope of the measurement data is changed to a corresponding domain, and fuzzification is performed, that is, the measurement data is transformed into a suitable These semantic values are labeled as fuzzy sets, and the fuzzy inputs are used to explain the determined measurement data with their fuzzy membership functions ([^]) on the fuzzy inputs; 15 (f · 2-2) input the model From this fuzzer module to a fuzzy inference mechanism, where the fuzzy inference mechanism essentially imitates human decision-making mechanisms and uses fuzzy inference rules to derive fuzzy outputs. The fuzzy logic inference rules from the fuzzy rule base rely on a set of semantics The rule summarizes the expert's eyesight and omissions. The fuzzy rule base consists of two domains of knowledge and goals; the fuzzy output is sent from the fuzzy inference mechanism to a fuzzy reduction model with 20 and (f.2-3). Here, the fuzzy reduction module generates a certain valid survey data. This data Yuan Hao expresses the possible distribution of the output of the inference model. 0 -38- This paper scale is applicable to China National Sample (CNS) A4. (210X297 mm) -------------- II ------ (Please read the notes on the back before filling out this page; &gt; Consumer Consumption Cooperative of Intellectual Property Bureau, Ministry of Economic Affairs Printed A8 Β8 __ C8 ^ —--- ^ D8 patent application scope ~ --- --- 30. "Hyperspectral image analysis and pixel spectral decomposition method" as described in item 13 of the patent application scope, where The Kalman filter is a universal holstering wave filter. 31. The "hyperspectral image analysis and pixel 5 spectral decomposition method" as described in item 30 of the scope of patent application, wherein the universal robust filter consists of the following The steps are composed of: (fl) receiving the measurement data of the pixel point from the atmospheric compensation module; (f.2) in the fuzzy logic module, using fuzzy logic inference method to check the measurement data, here, the fuzzy logic reasoning method Selectively reject the measurement based on the abundance of logic rules Data or correct the measurement data, or acknowledge the measurement data; (f ") output the corrected measurement data or errors to the preprocessing module, where the preprocessing module performs the calculation of the state transition matrix and the measurement matrix; 15 (f · 4) The state transition matrix is sent from the preprocessing module to the state vector prediction module 'send the previous state vector from the state vector update module to the state vector prediction module' Here, the state vector prediction module performs State vector prediction, that is, the content estimation of the next pixel; (f.5) The state transition matrix is sent from the preprocessing module to the covariance 20 propagation module, where the covariance propagation module calculates the current estimation error. Covariance, (£ 6) The measurement matrix and the current measurement vector are sent from the preprocessing module to the measurement residual calculation module, where the measurement residual calculation module receives the state vector prediction value from the state vector prediction module , And passed from • 39- this paper is again applicable to China National Standards (CNS) Α4 specifications (210 ×: 297 mm) (Please read the note on the back first (Please fill in this page again)-Install j, 1T line 3375 A8 B8 C8 D8 Patent application scope 10 15 Printed by the Intellectual Property Bureau of the Ministry of Economic Affairs Employee Cooperatives 20 Print out the current measurement vector minus the measurement matrix and predict the state vector The product of the values is used to calculate the measurement residual; (0) The covariance of the current estimated error is sent from the covariance propagation module to the optimal gain calculation module, where the optimal gain calculation module calculates the optimal gain; (f .8) send the optimal gain from the optimal gain calculation module to the covariance update module, where the covariance update module updates the covariance of the estimated error; (f.9) the covariance of the estimated error The updated value is sent from the covariance update module to the covariance propagation module; and (Π0) sends the optimal gain from the optimal gain calculation module to the state vector update module, where the state vector update module receives from the The measurement residual of the measurement residual calculation module performs state vector update, that is, the content estimator of the next pixel. 32. The "Hyperspectral Image Analysis and Pixel Spectrum Decomposition Method" as described in item 31 of the scope of patent application, wherein step (f.2) consists of the following steps: (f.2-1) send the measurement data To a fuzzer module, where the fuzzer performs scalar mapping, that is, transforms the range of the measurement data into a corresponding universe of speech, and performs modularization, that is, the measurement data is transformed into appropriate semantic values These semantic values are labeled as fuzzy sets, and the fuzzy inputs and their membership functions ([0]) are used to interpret the determined measurement data for the obtained fuzzy inputs; 5 (£ 2-2) remove the fuzzy inputs from the fuzzer module Send to a fuzzy reasoning mechanism, here, this model Xiang reasoning mechanism essentially imitates human decision-making Qin (please read the precautions on the back before filling this page)-installed I The well-defined paper size applies to China National Standards (CNS) Y 5 5 mm; Introduced by machine =: = inference rules_roll out, the t logic rules from the fuzzy rule base acquired expert goals and strategies with the help of group semantic rules. Gu domain knowledge and target composition And $ ,,, (£ 2-3) sent the lion output from the Laibu reasoning mechanism to a fuzzy reduction scheme. Here, the module _ original module generated-the effective measurement data, and the reasoning expressed the reasoning. The possible distribution of round $ 0 33. As described in the r scope of the patent application, the r hyperspectral map t analysis and pixel 10-frequency "77 solution / i:" 'its t this Kahman filter II is- Universal Robust Wavebreaker. ^ 34. The "Hyperspectral I Analysis and Pixel Spectrum Decomposition Method" as described in the scope of application for patent 33 items, wherein the universal robust filter consists of the following steps: (fl) receiving the atmospheric compensation module from the 15 measurements of pixels; (f.2) In the fuzzy logic module, the fuzzy logic inference method is used to check the measurement data; here, the fuzzy logic inference method selectively decides to reject the measurement based on rich fuzzy logic rules. Data or correct the measurement data 'or acknowledge the measurement data; 20 (G) outputs the corrected measurement data or error flag to the pre-processing module, where the pre-processing module performs the calculation of the state transition matrix and the measurement moment barrier U4) The state transition matrix is sent from the pre-processing module to the state vector prediction module, and the previous state vector is sent from the state vector update module to the -41- paper method. (Publication 297) 503375 Printed clothing A8 B8 C8 D8 by the Consumer Cooperatives of the Intellectual Property Bureau of the Ministry of Economy Module, here, the state vector prediction module performs state vector prediction, that is, the content estimator of the next pixel; (£ 5) sends the state transition matrix from the preprocessing module to the covariance propagation module, here, The covariance propagation module calculates 5 covariances of the current estimation error; (f.6) sends the measurement matrix and the current measurement vector from the preprocessing module to the measurement residual calculation module, where the measurement residual calculation module receives The state vector prediction value from the state vector prediction module, and calculate a measurement residual by subtracting a 10 product of the measurement matrix and the state vector prediction value from the current measurement vector; (f.7) the current estimation The covariance of the error is sent from the covariance propagation module to the optimal gain calculation module, where the optimal gain calculation module calculates the optimal gain; (f.8) sends the optimal gain from the optimal gain calculation module Go to the covariance update module, where the covariance update module updates the covariance of the estimated error; (f.9) update the covariance of the estimated error from the covariance update module To the covariance propagation module; and (f.10) sending the optimal gain from the optimal gain calculation module to the 20 state vector update module, where the state vector update module receives the measurement residual calculation module The measurement residual and update the state vector ', that is, the content estimator of the next pixel. 35. The "Hyperspectral Image Analysis and Pixel Spectrum Decomposition Method" as described in item 34 of the scope of the patent application, wherein step (f.2) consists of the following steps: -42- This paper scale is applicable to the Chinese National Standard (CNS ) A4 size (210 X 297 mm) ----------- install ------- order i (Please read the precautions on the back before filling this page) # k 州 375 Scope of patent application 10 15 Printed by the Consumer Cooperatives of the Intellectual Property Bureau of the Ministry of Economic Affairs 20 (£ 2-1) This measurement data is sent to a fuzzer module. Here, the fuzzer guesses the scalar _. The range is converted to a corresponding universe of discourse, and is further gelatinized, that is, the copper data is transformed into appropriate #semantic values. These semantic values are labeled as fuzzy sets. (F.2-2) Send the fuzzy input from the fuzzer module to a fuzzy inference mechanism, where the fuzzy inference mechanism essentially imitates human decision-making mechanism, And use fuzzy inference rules to derive fuzzy output. The fuzzy logic inference rules from the fuzzy rule base assist the dictation rules to capture the goals and strategies of experts. The model rule base is composed of application domain knowledge and goals; and &gt; ( f.2o) The fuzzy output is sent from the fuzzy inference mechanism to the paste reduction module. Here, the fuzzy reduction module generates a certain valid measurement data, which best represents the possible analysis of the inferred fuzzy output. Cloth. 36. The "high-resolution analysis and pixel spectral decomposition method" described in item 3 of the scope of the patent application, which causes the genetic algorithm decomposition to consist of the following steps: (1) a coding module randomly generates a set of binary strings Set, the set of binary strings represents the content estimate related to the pixel point of the hyperspectral image cube, the genetic algorithm operates on the set of binary strings, and the set of unary substrings is sent to a decoder Module; (2) The decoding module decodes the set of binary strings, and the output of the decoding module is about the pixels of the super spectrum image cube (please read the precautions on the back before filling this page)- '' Order-43- 503375 Printed by A8 B8 C8 ... ........— &quot; &quot; &quot;-&quot; &quot; &quot; -..... .. .......... ......, ____i 13S Sixth, the scope of patent application ~ ----= content estimation set, this set of content estimation set is sent to a fitness calculation module, the content estimation gives the The percentage of each material of interest contained in the pixel order, eight (3) suitable for 3 degrees, different for the pull block, different for each content estimated fitness value, 5 in the fitness calculation module order, the index function is taken as Binary string (also known as chromosome), Qian Huijian, weaving the value of the rescaling function $ to fit the previous algorithm, the fitness value is based on all possible solutions represented by the string Performance answer. The better the content of the encoded string is, the higher the fitness value is. The fitness value is then sent to 10 thin modules. (4) The judgment process is performed to determine whether to terminate the evolution. The discriminant index is defined as the total evolution algebra. When the genetic algorithm is recursive to the total evolution algebra, it is selected to make a binary string with the largest fitness value as the solution and the genetic algorithm exits evolution, and its corresponding content estimation vector 15 is the content estimation of the pixel; (5) in the breeding module, breeding is performed based on the fitness output from the fitness calculating module, and here, the breeding is based on the survival of the fittest The rule is that these binary strings with high fitness values will be copied in large numbers in the new generation. Once these binary strings are reproduced or copied as 20 for the next generation, the binary string will be used in the pairing set for another two. (6) In the mating module, the head and tail of the binary string (chromosome) are exchanged to form a substring set, and mating provides a mechanism for the binary ^ symbol string. Random process to mix and match their desired products (please read the notes on the back before filling out this page) -........ I ------ g I II 1 ..... ... · 1-1 —I— I ml, 1T line -44- This paper size is applicable to China National Standard (CNS) Α4 specification (210 × 297 cm) Scope of patent application 10 15 Printed by the Intellectual Property Bureau of the Ministry of Economic Affairs and Consumer Cooperatives 20 qualitative 'In this random process, the first reference, the collection: choose two newly generated strings? The second choice-an exchange position; the third , Exchange this exchange = in the mutation module towel, even · change in-heterodyne string bit field value. The difference is that any kind of simple bit of mosquitoes is safe. = 'The probability of occurrence of this mutation is extremely low, so that there is an average mutation in the word set, and 1⑻ the new binary string set Send it to the solution module, and then press (2) '(3)' (4), (5), ⑹'⑺, and ⑻ step performer. 4 Apply for the "Hyperspectral Image Analysis and Pixel Spectrum Decomposition Method" described in Item 5 of the Patent Scope, where the legacy method decomposition consists of the following steps: (1) A coding module randomly generates a set of binary strings Set, the binary string represents the 1¾ content estimate related to the pixel point of the hyperspectral image cube, the widow's transformation method operates on the H string, and the set of unary strings is sent to a Xie Ma Mo Yi; (2) The decoding module decodes the set of binary characters _, and the output of the decoding module is the content estimation set about the pixel points of the cube of the super-spectrum image. Fitness calculation module, the content estimate gives the percentage of each material of interest contained in the pixel; (3) the fitness calculation module calculates the fitness value of each content estimate, in the fitness calculation module The indicator function takes the binary string (also called the chromosome) and returns a value, and then the value of the indicator function is -45. This paper again uses the Chinese national standard (CNS) A4 specification (210X297) Please read the precautions before filling in the book <Page>. The fitness is adapted to the genetic algorithm. The fitness value is based on the sub-string. The answer to the performance of all possible solutions, the better the content of the encoded string is estimated, the higher the fitness value is, and the fitness value is sent to a breeding model; (4) the discriminating process is performed to marry whether to terminate the evolution Here, the discriminant index is defined as the total evolution algebra. When the genetic algorithm recurses to the total evolution algebra, it selects a binary string with the maximum fitness value as the solution and the genetic nose method exits evolution, and its corresponding The content estimation vector of is the content estimation of the pixel; (5) In the breeding module, based on the fitness recording from the fitness calculation module, the dip is dumped. Here, the button is based on the law of survival of the fittest, that is, These binary characters with high fitness values will have a large S copy in the new generation. Once these binary strings are reproduced or copied for the next generation, the binary word Strings will undergo two other types of operations in the mating set order, namely mating and mutation. Thus, breeding; (6) In the parental matching module, the substring set is formed by swapping the head and tail of the binary string (chromosome), Mating provides a mechanism for the binary financial string to mix and match its desired qualities through a random process. In the random process, first, two simple money-generating strings are selected from the matching set Yin formed by the promiscuous block; Second, randomly select a swap position along these two strings; third, swap all characters after the swap position; ⑺ In the mutation module, lie and change the value at the position of the transposed string , This mutation is for any material level _ long loss-a kind of insurance 5 A8 B8 C8 D8 patent scope measures, the probability of this mutation is extremely low, so that only one string in the set of strings will mutate on average; and (8 ) Send the new set of binary strings to the decoding module, and then perform steps (2) '(3), (4), (5), ⑹, ⑺, and (8). 5 38. According to the "Hyperspectral Image Analysis and Pixel Spectrum Decomposition Method" described in item 9 of the scope of the patent application, the decomposition algorithm decomposition consists of the following steps ... (1) An encoding module is randomly generated and A set of binary strings, the set of binary strings representing the content estimate related to the pixel point of the hyperspectral image cube, the genetic algorithm performs a 0 operation on the set of binary strings, and the set of unary strings is Sent to a decoding module; (2) the decoding module decodes the set of binary strings, and the output of the decoding module is the content estimation set about the pixels of the super-spectrum image cube, and the content estimation set is sent To a fitness calculation module, the content estimate gives the percentage of each interesting material contained in the pixel; '(3) The fitness calculation module calculates the fitness value of each content estimate. In the degree calculation module, the indicator function takes the binary string (also called chromosome) and returns a value, and then the value of the indicator function is evaluated by Printed by the Ministry of Standards and Technology Bureau's Consumer Cooperatives 20 = Shooting as a suitability appropriation method, which is a response based on the performance of all possible solutions represented by the word. Well, the fitness value is also higher, and the fitness value is sent to a breeding module again. (4) The discrimination process is performed to determine whether to terminate the evolution. Here, the discrimination index is defined as the total number of evolutionary generations. The total evolution 503375 A8 B8 C8 D8 VI. Patent application scope Printed by the Consumer Cooperatives of the Intellectual Property Bureau of the Ministry of Economic Affairs =, select one of them as a binary string with the largest fitness value, and solve the genetic algorithm such as evolution, its corresponding The content estimation vector of the pixel is the content estimation of the pixel; in the mtr complex block, the basic fitness calculation module 5 ^ the appropriate j degree is lost, and the thinning is performed. Here, the reproduction is based on the rules of the fittest child. gp 'These binary strings with high fitness values will have a large number of copies in the new generation,-once these binary escape strings ㈣ M copy as the next generation to use this-the base string will Two other types of operations are performed in the paired set, that is, mating and mutation, so as to reproduce; 10 (6) In the mating mode + 'by forming the substring set by swapping the head and tail of the binary string (chromosome), mating Provide a mechanism for the binary string to mix and match its desired qualities through a random process. First, in the random wealth, two new generated strings are selected from the matching set of the block; second, , Along these two strings 15-Zhiqi machine selection-a swap position; Third, swap all characters after the drum change position; (7) In the mutation module, occasionally change the value at a specific string position, This mutation is a kind of insurance against the permanent loss of any simple bit. The probability of occurrence of war mutation is extremely low, so that only one string in the set of strings will mutate on average; and (8) the new one Send the set of string of characters to the decoding module, and then press (2) '(3), (4), (5), (6), (7), and the step performer. 39. For example, "High-spectrum_Analysis and Pixel Spectrum Decomposition Method", which is listed in item 13 of the scope of patent application, where the genetic algorithm decomposition is composed of the following steps: -48- This paper scale is applicable to China National Standards (CNS) A4 Specifications (210X297mm) (Please read the notes on the back before filling this page}-Gutter (1) An encoding module randomly generates a set of binary character strings. The binary character string represents the estimated value associated with the pixel of the hyperspectral image cube. The genetic algorithm operates on this set of binary character strings. And the set of binary strings is sent to a decoding module; 5 The decoding module decodes the binary string, and the round of the decoding module its g 卩 is a set of 2 content estimates for the super pixel This set of content estimation set is sent to a fitness calculation module, which gives the percentage of each material of interest contained in the pixel; 10 (3) The fitness calculation module calculates the content of each content estimation Fitness value. At the appropriate δ degree, if there is a model order, the indicator function takes the binary string (also called chromosome) 'and returns a value, and then maps the value of the indicator function to fitness to adapt to the genetic Algorithm, the fitness value is an answer based on the performance of all possible solutions represented by the string, the better the 15 content estimate of the encoded string is The higher the value, the fitness value is sent to a breeding module again. (4) A discrimination process is performed to determine whether to terminate the evolution, and the discrimination is made by evaluating the difference between the unary strings. When the difference between the strings is less than a pre- When the value is set, the genetic algorithm exits evolution. After that, 20 chooses a binary string with the highest fitness value as the solution, and its corresponding content estimation vector is the content estimation of the pixel; (5) in the breeding module Based on the fitness output from the fitness calculation module, breeding is performed. Here, the breeding is based on the law of survival of the fittest, that is, these binary strings with high fitness values will be in the new generation -49- Chinese patent Once these two complement strings are copied by Wei or copied as a pair # 合 _, that is, parental mating and mutation, thereby breeding; dumb 5 ⑷ in the mating model, the exchange of the binary character contention (dye tail to form a substring set , Mating is the second quality, in; b shop through a random process to mix and match its desired product; 4 ;: in the 'first' from the complex "the match formed by the block" two ^ out _ newly generated characters _; Secondly, along these two 10 彳 == 择-编 mosquito; Third, the fiber exchange position is changed by the 魏 魏 t t occasionally changed in the value of a specific string bit =, the mutation is strange to any simple bit A long-lost insurance: the probability of occurrence of the mutation is so low that only one string in the string set mutates on average; and 15, ~ (δ) sends the new binary string set to the decoding module, Then press (2), (3), ⑷, (5), ⑹, ⑺, and ⑻ step performers. 40. For example, “High-Spectrum County Analysis and Pixel Spectrum Decomposition Green” of the patent application @ 第 17 顾 述, Which t the genetic algorithm decomposition consists of the following steps: 20 (1) a The encoding module randomly generates a set of binary strings. The set of binary strings represents the content estimate related to the image point of the hyperspectral image cube. The genetic algorithm operates on the set of binary strings, and the group The binary string is sent to a decoding module; (2) The decoding module decodes the set of binary strings, and the output of the decoding module is the Qin application patent fan garden about the super-spectral image cube pixels. Mei 4 butterfly fitness value, the scale function takes the binary string (also adapted to the genetic algorithm: == prepaid, all possible solutions to the table's energy solution Θ answer, _ code string of 10 15 Intellectual Property Bureau of the Ministry of Economic Affairs The better the employee ’s consumer cooperative prints the content estimate, the higher the fitness value is, and the fitness value is sent to a breeding module; (4) The discrimination process is performed to determine whether to terminate the evolution. Here, the discrimination index; t means Total evolution algebra, when the genetic algorithm recurs to the total evolution algebra, select its t-binary strings with the largest fitness value as the solution and The legacy method exits evolution, and its corresponding content estimation vector is the content estimate of the pixel; (5) In the breeding module, breeding is performed based on the fitness output from the fitness calculation module, and here, the breeding It is based on the law of survival of the fittest, that is, these binary strings with high fitness values will be copied in a large number of new generations. Once these binary words: runes are reproduced or copied for use as the next generation, the binary string will be Perform two other types of operations in the pairing set, that is, mating and mutation, to reproduce; (6) In the mating module, exchange the head and tail of the binary string (chromosome) to form a substring set, and mate to the binary Character contention provides a mechanism to mix and match its desired product through a random process-51-this paper ^ AilL Financial Relations (CNS) A4 specification (210X297 public ^ Liu Zhongjing patent scope) To match the block face, the matching = I select two newly generated strings; second, randomly select the two strings along the two-the position of failure; third, exchange the exchange All characters in a set of several; ⑺ average differential mode as well as in the set of character strings Choi, Qi becomes even in - rpm = value given bit string. The mutation is applied to any drunk bitch—a kind of safety belt, the probability of occurrence of the mutation is extremely low, and only one string is mutated; and js) sends the new binary string set to the decoding module, shouts Press (2) '(3), (4), (5), ⑹, ⑺1 and ⑻ step performers. Printed by the Consumer Property Cooperative of the Intellectual Property Bureau of the Ministry of Economic Affairs 丨 · Sheet 5