CN111553237B - LJ1-01 night lamplight data denoising method based on polymorphic superposition Gamma distribution - Google Patents
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Abstract
The application provides an LJ1-01 night lamplight data denoising method based on polymorphic superposition Gamma distribution, which comprises the following steps: acquiring LJ1-01 night light data; constructing a polymorphic superimposed Gamma distribution function for representing the LJ1-01 night light data; solving the weight of each superposition state; selecting pure noise as a noise subset from the LJ1-01 night light data; separating out weights of the mixed distribution in the noise subset; calculating the probability density of the LJ1-01 night lamplight data after noise is removed; constructing an effective abundance function for representing the ratio of the probability density after noise rejection to the original probability density; and calculating effective lamplight data according to the effective abundance function. The LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution provided by the application can be used for rapidly removing noise of LJ1-01 night light data, effectively removing noise in different environments, has better robustness and has good practicability and effectiveness in practical application.
Description
Technical Field
The application belongs to the technical field of remote sensing data, and particularly relates to an LJ1-01 night lamplight data denoising method based on polymorphic superposition Gamma distribution.
Background
Night light remote sensing data is a direct reflection of human night activities and has extremely high correlation with population, GDP and energy consumption. The captured night light spot can obtain night light data with geographic coordinates after being subjected to atmosphere correction, geometric correction and the like. In 1992-2013, the linear scanning business system (OLS) carried by the national Defense Meteorological Satellite (DMSP) realizes the first utilization of night light data, and the data is published by rejecting non-city lights and can be directly used for related research, which is the starting point of night light remote sensing. After 2013, the data are provided by a visible light infrared imaging radiometer (NPP-VIIRS) carried by Suomi national polar orbit partnership satellites, compared with MSP/OLS, the NPP-VIIRS can acquire visible light and near infrared images at night, the spatial resolution of 5 times and the radiation resolution of 250 times are improved, and the problem of oversaturation overflow of MSP/OLS lamplight data is solved. The night light image with high spatial resolution, high time resolution and high radiation resolution has higher scientific research value and wider research direction. In 6 months 2018, a new generation of remote sensing science experiment microsatellite LJ1-01 satellite commonly developed by the university of Chinese Wuhan and CGSTL (vinca satellite technologies Co., ltd.) has a resolution of 130 meters and a breadth of 250km, and can be used for drawing global night light images in 15 days under ideal conditions. Compared with NPP-VIIRS and DMSP/OLS, LJ1-01 has better utilization space and research value, and can meet the task requirements of finer ship extraction, factory extraction, city edge demarcation and the like, but more stray light noise can appear in night light data under different weather conditions and different surface reflectivity. The stray light noise is the target light such as strong energy of the sun, diffuse reflection of cloud layers, abnormal paths with high earth surface reflectivity and the like, and the stray light noise is mixed with normal energy distribution, so that an image obtained by the sensor has more noise, extraction and analysis of total light value statistics, boundary definition of normal earth surface energy distribution and the like are seriously influenced, and the utilization efficiency of later-stage data is seriously influenced.
Stray light noise in LJ1-01 affects the accuracy of analysis, extraction and evaluation of data, and therefore must be rejected. DMSP eliminates noise effects by removing stable products of the broad spectral band (0.4-1.1 um). Although VIIRS can be designed to eliminate light rays having an incident angle greater than 28 degrees using a Rotating Telescope Assembly (RTA) instead of a scanning mirror, noise problems can occur in high latitude areas. In contrast, LJ1-01 uses stray light removal methods including anisotropic baffles and internal structural optimization to properly eliminate solar stray light, but still has a small portion of light from surface reflection and atmospheric scattering contaminating the raw light data.
The light data precision of different areas at different times is mainly influenced by the diffuse reflection of cloud layers, the refraction of partial large-particle pollutants, the reflectivity of the adjacent ground surface and the like. If these noises are removed, LJ1-01 can provide better service products, such as: GDP index, carbon emission index, and urban void fraction index, etc. The method is used for researching the rejection noise in LJ1-01 data, and has important significance and reference value for mining and utilizing the data. Therefore, the LJ1-01 night light probability distribution model is studied, and a polymorphic superposition Gamma distribution model [23-26] is determined according to the noise probability distribution condition in LJ1-01 in each region, so that noise in the region is removed.
The prior related technology mainly comprises the following 2 methods: a large water area threshold denoising method and a normal distribution fitting cut-off method;
1. threshold denoising method for large water area
Subjective judgment is taken as the main principle, and no interference of artificial activities in a large water area is subjectively considered, and no active lamplight data exists, so that lamplight brightness values in the large water area are all noise. Firstly, one or more large water areas are selected; second, the median, mean, variance, etc. of the light values in these areas are calculated. Finally, the threshold is defined as a median, mean, or other characteristic value.
The method can obviously and effectively remove most of noise, but does not meet the standard data generation requirement in detail processing.
2. Normal distribution fitting cut-off method
And (3) taking single probability density fitting as a main part, recognizing probability density distribution of night lamplight data in an area, taking errors in 1/2/3 times of the normal distribution as cut-off items, and eliminating noise in the lamplight data.
The method is based on a mathematical principle, can reflect the distribution rule of the lamplight data to a certain extent, and can remove most of noise, but the method has the problem that the lamplight data has no negative value, the abscissa is less than 0, the normal distribution probability density is not necessarily 0, and the method has certain effect, but the method is not in accordance with reality in theory, and meanwhile, certain accidental happens when the middle errors of different multiples are used as cut-off items, so that the denoising precision is suddenly high and suddenly low.
Disclosure of Invention
The application aims to provide an LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution, aiming at the defects of the prior art, and the method comprises the following steps:
acquiring LJ1-01 night light data;
constructing a polymorphic superimposed Gamma distribution function for representing the LJ1-01 night light data;
solving the weight of each superposition state;
selecting pure noise as a noise subset from the LJ1-01 night light data;
separating out weights of the mixed distribution in the noise subset;
calculating the probability density of the LJ1-01 night lamplight data after noise is removed;
constructing an effective abundance function for representing the ratio of the probability density after noise rejection to the original probability density;
and calculating effective lamplight data according to the effective abundance function.
Preferably, the acquiring LJ1-01 night light data includes: and acquiring the LJ1-01 night light data according to the time sequence.
Preferably, the polymorphic stacked Gamma distribution function formula is:
wherein g (i, x) i ) The superposition state is integrally approximated for each chi-square distribution of the lamplight dataRate distribution, f (x i ) Chi-square probability distribution for different parameters i For different parameters χ 2 Distributed weights, i e (1, 2..n) is χ 2 Parameters of distribution g i (i,x i ) For different parameters χ 2 The probability of occupation in the whole is distributed.
Preferably, the solving the weights of each superposition state includes:
when solving n unknown weights, if m observed values are only adopted, adopting least square adjustment to solve the approximate solution of the weights.
Preferably, when n > m, the solution of the weight is a least squares approximation solution under conditional adjustment; when n is less than or equal to m, the solution of the weight is the only solution under the indirect adjustment according to the least square criterion.
Preferably, the formula of the probability density of the LJ1-01 night light data after noise elimination is as follows:
wherein g 0 (i,x i ) To reject the probability density of the light data after noise, g (i, x i ) G, overlapping state integral probability distribution for each chi-square distribution of the lamplight data j (i,x i ) For the j-th noise subset A j Is a probability density of (c) for a given probability density.
Preferably, the formula of the effective abundance function is:
wherein,for the j-th noise subset A j Center of gravity of weight function, +.>Is the center of gravity of the weight function of the original lamplight data.
Preferably, said calculating effective light data from said effective abundance function comprises: and eliminating light data biased to noise according to the content preset threshold value of the effective abundance function, wherein the rest part is the effective light data after noise elimination.
The LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution provided by the application can be used for rapidly removing noise of LJ1-01 night light data, effectively removing noise in different environments, has better robustness and has good practicability and effectiveness in practical application.
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In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of a LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution, which is provided by the application;
FIG. 2 is a schematic diagram of the comparison of example 1 before and after the LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution;
FIG. 3 is a schematic diagram of the comparison of example 2 before and after the LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution;
FIG. 4 is a schematic diagram of the comparison of the embodiment 3 before and after the LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution;
FIG. 5 is a schematic diagram of the comparison of the embodiment 4 before and after the LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution;
FIG. 6 is a schematic diagram of the comparison of the embodiment 5 before and after the LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution;
Detailed Description
Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
Other advantages and effects of the present disclosure will become readily apparent to those skilled in the art from the following disclosure, which describes embodiments of the present disclosure by way of specific examples. It will be apparent that the described embodiments are merely some, but not all embodiments of the present disclosure. The disclosure may be embodied or practiced in other different specific embodiments, and details within the subject specification may be modified or changed from various points of view and applications without departing from the spirit of the disclosure. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the following claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the present disclosure, one skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. In addition, such apparatus may be implemented and/or such methods practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should also be noted that the illustrations provided in the following embodiments merely illustrate the basic concepts of the disclosure by way of illustration, and only the components related to the disclosure are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided in order to provide a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
The embodiment of the disclosure provides an LJ1-01 night light data denoising method based on polymorphic superposition Gamma distribution. The LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution provided by the embodiment can be executed by a computing device, the computing device can be realized as software or a combination of the software and hardware, and the computing device can be integrally arranged in a server, terminal equipment and the like.
Referring to fig. 1, in an embodiment of the present application, the present application provides a method for denoising LJ1-01 night light data based on polymorphic superimposed Gamma distribution, the method comprising the steps of:
s101: acquiring LJ1-01 night light data;
s102: constructing a polymorphic superimposed Gamma distribution function for representing the LJ1-01 night light data;
s103: solving the weight of each superposition state;
s104: selecting pure noise as a noise subset from the LJ1-01 night light data;
s105: separating out weights of the mixed distribution in the noise subset;
s106: calculating the probability density of the LJ1-01 night lamplight data after noise is removed;
s107: constructing an effective abundance function for representing the ratio of the probability density after noise rejection to the original probability density;
s108: and calculating effective lamplight data according to the effective abundance function.
According to the LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution, provided by the embodiment of the application, the noise of the LJ1-01 night light data can be quickly removed as required, so that effective light data can be obtained, and a more accurate data basis can be provided for researching human night activities.
In the embodiment of the present application, the step S101 of obtaining LJ1-01 night light data includes: and acquiring the LJ1-01 night light data according to the time sequence.
In the embodiment of the application, one or more than one quantity of LJ1-01 night light data can be acquired according to the requirement. When a plurality of LJ1-01 night light data are acquired, in order to ensure the representativeness of a data sample, the LJ1-01 night light data can be acquired according to a time sequence, and the time intervals between two adjacent LJ1-01 night light data are equal, for example, one LJ1-01 night light data is acquired at 1min, 3min, 5min and 7min, and four LJ1-01 night light data are arranged according to the time sequence.
In the embodiment of the present application, the polymorphic superimposed Gamma distribution function formula in step S102 is:
wherein g (i, x) i ) Superimposed overall probability distribution, f (x) i ) Chi-square probability distribution for different parameters i For different parameters χ 2 Distributed weights, i e (1, 2..n) is χ 2 Parameters of distribution g i (i,x i ) For different parameters χ 2 The probability of occupation in the whole is distributed.
In the embodiment of the present application, the solving the weights of each superposition state in step S103 includes:
when solving n unknown weights, if m observed values are only adopted, adopting least square adjustment to solve the approximate solution of the weights.
Specifically, in the embodiment of the application, when n is greater than m, the solution of the weight is a least squares approximate solution under the condition adjustment; when n is less than or equal to m, the solution of the weight is the only solution under the indirect adjustment according to the least square criterion.
In the embodiment of the application, in step S104, the data of the LJ1-01 night light is displayedSelecting pure noise as noise subset, specifically selecting representative pure noise at one place in the same LJ1-01 night light data as noise subset A 1 So-called concrete representativeness, may be selected according to a plurality of dimensions, for example, pure noise having most of the noise characteristics among all noises may be selected as a noise subset to represent all noises.
Further, the calculated probability density of the noise subset is:
similarly, the probability density of the kth noise subset is:
wherein ε is i k For the kth noise subset A k At each χ 2 The weights occupied on the distribution, at the same time, the sum of the weights must be 1,
in the embodiment of the application, the probability density formula of LJ1-01 night light data after noise elimination in step S106 is as follows:
wherein g 0 (i,x i ) To reject the probability density of the light data after noise, g (i, x i ) G, overlapping state integral probability distribution for each chi-square distribution of the lamplight data j (i,x i ) For the j-th noise subset A j Is a probability density of (c) for a given probability density.
In the embodiment of the present application, the formula of the effective abundance function in step S107 is:
wherein,for the j-th noise subset A j Center of gravity of weight function, +.>Is the center of gravity of the weight function of the original lamplight data.
In the embodiment of the present application, calculating effective lamplight data according to the effective abundance function in step S108 includes: and eliminating light data biased to noise according to the content preset threshold value of the effective abundance function, wherein the rest part is the effective light data after noise elimination.
For example: let G (i, x) i ) If the effective light data is more than or equal to 90%, partial data with the effective abundance function more than or equal to 90% can be screened, noise filtering can be carried out on the original data only by setting a proper threshold value, most noise light spots are removed, and the light data after noise removal is applied to aspects of city index, GDP estimation, object extraction and the like, so that a better result can be obtained.
The LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution is applied in the following specific embodiment.
Fig. 2-5 are schematic diagrams of beijing city, cheng du city, wu mu qi city and bei qu city, which are compared before and after the method provided by the application is used, wherein a (1), a (2) and a (3) are verification areas, a (3) is a sample subset, B (1), B (2) and B (3) are areas corresponding to the verification areas after noise elimination, and the denoising capability of the method has obvious advantages.
Fig. 6 is a schematic diagram of a marine control on the sea surface of northern bay, where a (1), a (2), and a (3) are three sea surfaces in the raw data, and B (1), B (2), and B (3) are corresponding positions after denoising. In a (1) and B (1), the positions of the vessels are relatively concentrated, and the lights between the vessels interfere with each other, so that the resolvability is poor. For the original data, only the relative discrete ships can be extracted by a morphological clustering method, and the denoised images can be used for extracting the complete shape. As shown in a (2), the high intensity light is reflected and refracted by the water surface, and thus the light reflected from the sea surface on the ship is extracted, and the extraction amount is far more than the true value. In B (2), the boat can be extracted while substantially eliminating noise. The effect on the individual vessels will be more severe when the two vessels are very close to each other. The method provided by the application can distinguish and extract the independent ships more effectively after removing noise reflection such as water reflection, scattering, refraction and the like.
Experiments show that in ship extraction, the effect of directly extracting the original data is poor, and different noises have great influence on the extraction precision. The specific extraction accuracy is shown in table 1.
The actual number of vessels is known in the areas (1), (2) and (3), and the original map and the number of vessels extracted after noise removal are obtained, the extraction accuracy is only 80% if the extraction method is directly used, and the number of extraction times in the area (2) is far greater than the actual number. Analysis shows that the recognition error is caused by the influence of noise, the effective precision of the region (2) after visual recognition is only 70%, and the minimum extraction precision of the ship extraction after denoising by the mixed chi-square distributed filtering method is 90%.
Table 1 marine vessel extraction accuracy for different regions
The LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution provided by the application can be used for rapidly removing noise of LJ1-01 night light data, effectively removing noise in different environments, has better robustness and has good practicability and effectiveness in practical application.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The name of the unit does not in any way constitute a limitation of the unit itself, for example the first acquisition unit may also be described as "unit acquiring at least two internet protocol addresses".
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.
The foregoing is merely specific embodiments of the disclosure, but the protection scope of the disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the disclosure are intended to be covered by the protection scope of the disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (5)
1. The LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution is characterized by comprising the following steps:
acquiring LJ1-01 night light data;
constructing a polymorphic superimposed Gamma distribution function for representing the LJ1-01 night light data;
solving the weight of each superposition state;
selecting pure noise as a noise subset from the LJ1-01 night light data;
separating out weights of the mixed distribution in the noise subset;
calculating the probability density of the LJ1-01 night lamplight data after noise is removed;
constructing an effective abundance function for representing the ratio of the probability density after noise rejection to the original probability density;
calculating effective lamplight data according to the effective abundance function;
the step of obtaining LJ1-01 night light data comprises the following steps: acquiring the LJ1-01 night light data according to the time sequence;
the polymorphic superposition Gamma distribution function formula is as follows:
wherein g (i, x) i ) Superimposed overall probability distribution, f (x) i ) Chi-square probability distribution for different parameters i For different parameters χ 2 Distributed weights, i e (1, 2..n) is χ 2 Parameters of distribution g i (i,x i ) For different parameters χ 2 The probability of occupation in the whole is distributed.
The formula of the effective abundance function is as follows:
wherein,for the j-th noise subset A j Center of gravity of weight function, +.>Is the center of gravity of the weight function of the original lamplight data.
2. The method for denoising LJ1-01 night light data based on polymorphic superimposed Gamma distribution according to claim 1, wherein the solving the weights of each superimposed state comprises:
when solving n unknown weights, if m observed values are only adopted, adopting least square adjustment to solve the approximate solution of the weights.
3. The LJ1-01 night light data denoising method based on polymorphic superimposed Gamma distribution according to claim 2, wherein when n is more than m, the solution of the weight is a least squares approximate solution under the condition of adjustment; when n is less than or equal to m, the solution of the weight is the only solution under the indirect adjustment according to the least square criterion.
4. The method for denoising LJ1-01 night light data based on polymorphic superimposed Gamma distribution according to claim 1, wherein the formula of probability density of LJ1-01 night light data after noise elimination is:
wherein g 0 (i,x i ) To reject the probability density of the light data after noise, g (i, x i ) G, overlapping state integral probability distribution for each chi-square distribution of the lamplight data j (i,x i ) For the j-th noise subset A j Is a probability density of (c).
5. The method for denoising LJ1-01 night light data based on polymorphic superimposed Gamma distribution according to claim 1, wherein calculating effective light data according to the effective abundance function comprises: and eliminating light data biased to noise according to the content preset threshold value of the effective abundance function, wherein the rest part is the effective light data after noise elimination.
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