CN109063606A - Mineralization alteration remote sensing information extraction method and device - Google Patents

Abstract

The invention provides a method and a device for extracting mineralization alteration remote sensing information, wherein the method comprises the following steps: the method comprises the following steps that firstly, remote sensing image data are obtained and processed by utilizing a mask technology to form basic data; secondly, performing histogram calculation processing on the basic data in a selected area to determine modeling data, extracting abnormal information from the modeling data by adopting a principal component analysis method, and respectively performing cutting and verification on the abnormal information by sequentially utilizing a normal distribution method and a multiple linear regression method; and step three, acquiring eigenvectors meeting the check according to an eigenvector algorithm extracted from the abnormal information, taking the eigenvectors as extraction parameters to perform space expansion mapping to extract new abnormal information in a large range, and then performing cutting processing on the new abnormal information and outputting the new abnormal information. The method can realize the work of cross-over type alteration remote sensing abnormal extraction in different landscape areas, is beneficial to more quickly and accurately selecting the target area for finding the mine, and can save time and manpower and physical force for the mineral investigation work.

Description

Mineralization alteration remote sensing information extraction method and device

Technical Field

The invention relates to the field of geological exploration, in particular to a method and a device for extracting mineralization alteration remote sensing information.

Background

The alteration remote sensing abnormal information is quantitatively extracted from remote sensing data by using a mathematical means and is used for representing the indication information of the near-ore alteration rock which is probably most relevant to mineralization. The alteration remote sensing abnormal information can be used as a mark for finding ores, has geological and spectral basis, and is proved and widely applied by practice.

The formation of mineral deposits is a gradual enrichment process of some useful element, and the mineral forming substances are usually carried and enriched by the mineral forming hot liquid. Near-mine surrounding rock alteration is the imprint left in the process of gradually enriching the mineral forming substances into the mine. The most common alterations are: silicification, sericitization, greenmud petrochemical, astragaloside, recrystallization and ferromanganese carbonation. Geologists assert that most of the mineral deposits are accompanied by the phenomenon of intergrowth alteration of their surrounding rocks, and that the extent of the altered zone is several times to tens of times greater than the extent of the distribution of the ore body. Although altered rock exists and not necessarily has ore, large and oversize endogenous mineral deposits generally have the existence of the altered rock in a strong and large range. It can be certain that the discovery of altered rock can indicate the direction of prospecting, increasing the chance of finding a deposit. Large metallic, non-metallic deposits found from alteration of surrounding rock are numerous, for example: most porphyry copper ores in North America and Russia, a plurality of scheelite ores in the United states and large aluminum ores in Utah, cupreous cuprite in China, large gold ores in Western Australia, large platinum ores in Mexico, corundum ores in Hassakestein, most tin ores in the world and the like, and the examples prove that the alteration phenomenon of the surrounding rock is of great significance as an ore finding mark. Therefore, finding the alteration of the surrounding rock becomes a crucial step. The traditional discovery of the alteration of the surrounding rock is realized by means of exploration. In Hunt and other leading laboratories in the 70 th 20 th century, a system published an article about the spectrum test result of mineral rock, and provides a spectrum basis for the abnormal information of the altered remote sensing to be used as a mineral exploration mark. Hunt successfully generated a "spectral signature map" (which facilitates understanding of the spectral signatures commonly encountered in TM and ASTER telemetry data) using measurements of approximately 300 granular minerals. From this, scientists apply remote sensing to geology, utilize characteristics such as remote sensing wide-angle, big eye, information are abundant, timing location, macroscopic observation, multiband, third dimension are strong, topographic features are obvious to study the geologic body on earth's top layer to provide help for studying geological structure, geological mapping, regional geological survey, mineral resources exploration, geological disaster monitoring etc..

At present, the application of the abnormal alteration remote sensing in the prospecting has made great progress and great success. The alteration remote sensing abnormity is a remote sensing information extraction method for extracting alteration minerals and combination thereof, the technology in arid regions tends to be mature, but the abnormity extraction in vegetation regions, loess coverage regions and other various landscape regions is very difficult, and the abnormal information is mostly hidden in vegetation or loess information and other various information, so that the abnormity is difficult to extract from the vegetation coverage regions or loess coverage regions and other landscape regions. There is no good method in other landscape areas such as loess-covered landscape area, and a processing method similar to that in arid area is generally adopted. The same processing method is adopted in other areas, weak alteration remote sensing abnormal information still cannot be identified in different landscape areas such as deeply-cut landscape areas, and the like, and the method is very limited in practical application.

Disclosure of Invention

In order to solve the problems that weak alteration remote sensing information cannot be identified in different landscape areas such as vegetation areas, loess coverage areas and the like in the prior art, so that the extraction is difficult and the like, the invention provides a method for extracting mineralization and alteration remote sensing information, which can expand the spatial scale of weak mineralization and alteration remote sensing abnormal information so as to enhance the mineralization and alteration remote sensing abnormality; and the problems that the alteration remote sensing abnormity extraction difficulty is high, the number of false abnormity is large, a large number of area type abnormity which is not related to ore finding is generated, and the ore finding is not targeted and the like in different landscape areas can be solved. The invention also provides a device for extracting the mineralization alteration remote sensing information.

The technical scheme of the invention is as follows:

a method for extracting mineralization alteration remote sensing information is characterized by comprising the following steps:

the method comprises the following steps that firstly, remote sensing image data are obtained and processed by utilizing a mask technology to form basic data;

secondly, performing histogram calculation processing on the basic data in a selected area to determine modeling data, extracting abnormal information from the modeling data by adopting a principal component analysis method, and respectively performing cutting and verification on the abnormal information by sequentially utilizing a normal distribution method and a multiple linear regression method;

and a third step of obtaining the eigenvector meeting the check according to an eigenvector algorithm extracted from the abnormal information, taking the eigenvector as an extraction parameter to perform space expansion mapping to extract new abnormal information in a large range, and then performing cutting processing and outputting the new abnormal information.

Furthermore, the remote sensing image data acquired in the first step is multiband remote sensing image data, preprocessing is performed after the remote sensing image data are acquired, and then basic data are formed by utilizing a mask technology, wherein the preprocessing comprises boundary removing processing and interference removing processing; carrying out wave band selection on the remote sensing image data to synthesize a false color image;

and in the third step, after new abnormal information is cut, abnormal filtering optimization is carried out, the grid and the vector are superposed by utilizing coordinate layering in combination with the false color image in the first step after optimization, and then a mineralized alteration remote sensing abnormal image suitable for human eye observation is output.

And further, the second step is to cut the abnormal information by utilizing normal distribution, the cut data is compared with the coincidence degree of the sample coordinate by adopting the central coordinate of the alteration abnormality, the regression square sum and the residual square sum are obtained by utilizing a multiple linear regression method to measure the regression effect, and the verification and the evaluation of the abnormal information are realized by combining with the test of the independent variable value.

Furthermore, the third step obtains the eigenvector meeting the check according to the eigenvector algorithm of the abnormal information extraction, and uses the eigenvector as an extraction parameter to perform space expansion mapping to the new abnormal information extraction in a large range, so as to obtain the eigenvector of the new abnormal, and then performs symbol discrimination processing on the eigenvector of the new abnormal of each band, and performs cutting processing after the symbol discrimination processing.

Further, after the remote sensing image data are obtained, preprocessing is carried out, then mask technology processing and linear stretching are utilized to form basic data, boundary removing processing of preprocessing is to remove boundary information by combining the remote sensing image data of each wave band with binary image processing technology, and interference removing processing of preprocessing adopts a ratio method, a cutting method, a Q value method and/or a spectral angle method;

and/or, the abnormal filtering optimization in the third step adopts a Q value method and a median filtering method for filtering in turn.

The invention also provides a mineralization alteration remote sensing information extraction device which is characterized by comprising a first device, a second device and a third device which are connected in sequence,

the first device acquires remote sensing image data and processes the remote sensing image data by utilizing a mask technology to form basic data;

the second device performs histogram calculation processing on the basic data in a selected area to determine modeling data, extracts abnormal information from the modeling data by adopting a principal component analysis method, and cuts and checks the abnormal information by sequentially utilizing a normal distribution method and a multiple linear regression method;

and the third device obtains the eigenvector meeting the check according to an eigenvector algorithm for extracting the abnormal information, takes the eigenvector as an extraction parameter to perform space expansion mapping to extract new abnormal information in a large range, and then performs cutting processing and outputting of the new abnormal information.

Further, the first device comprises an image acquisition device, a preprocessing device, a basic data generation device and a band selection processing device connected with the image acquisition device, wherein the image acquisition device is used for acquiring original remote sensing image data; the remote sensing image data is multiband remote sensing image data; the image preprocessing device is used for preprocessing the acquired remote sensing image data, and the preprocessing comprises boundary removing processing and interference removing processing; the basic data generating device processes the preprocessed data by using a mask technology to form basic data; the band selection processing device is used for carrying out band selection on the remote sensing image data and synthesizing a false color image;

the third device comprises a parameter extraction device, an extended anomaly extraction device, an anomaly cutting device, a filtering optimization device and a synthesis device which are sequentially connected, wherein the parameter extraction device obtains and extracts the eigenvectors meeting the verification according to an eigenvector algorithm extracted by the anomaly information; the extended anomaly extraction device performs space extension mapping on the parameters extracted by the parameter extraction device to extract new anomaly information in a large range; the abnormal cutting device cuts the new abnormal information; the filtering optimization device performs abnormal filtering optimization processing; and the synthesis device combines the abnormal filtering optimization processing result with the false color image output by the band selection processing device of the first device, superposes the grid and the vector by utilizing coordinate layering, and further outputs a mineralized alteration remote sensing abnormal image suitable for human eye observation.

Further, the second device comprises a modeling device, an anomaly extraction device, an anomaly optimization device and an anomaly verification device which are connected in sequence, wherein the modeling device is connected with the basic data generation device of the first device and is used for performing histogram calculation processing on the basic data in a selected area to determine modeling data; the anomaly extraction device adopts a principal component analysis method to extract anomaly information of the modeling data; the abnormal optimization device cuts abnormal information by utilizing normal distribution; and the abnormal checking device is connected with the parameter extraction device of the third device and is used for comparing the coincidence degree of the secondary alteration abnormal center coordinates of the cut data and the sample coordinates, obtaining regression square sum and residual square sum by utilizing a multiple linear regression method to measure the regression effect and combining with the test of the independent variable value to realize the check and evaluation of the abnormal information.

Furthermore, the third device further comprises a data discrimination device, the extended anomaly extraction device is connected with the anomaly cutting device through the data discrimination device, the extended anomaly extraction device obtains the eigenvector of the new anomaly, the data discrimination device performs symbol discrimination processing on the eigenvector of the new anomaly of each waveband, and the anomaly cutting device performs cutting processing after the symbol discrimination processing.

Further, the image preprocessing device comprises a boundary removing processing module and an interference removing processing module, wherein the boundary removing processing module is used for performing boundary removing processing on the original remote sensing images of all wave bands by combining a binary image processing technology to obtain boundary-removed remote sensing image data; the interference removing processing module is used for removing interference on the remote sensing image data after the boundary is removed by adopting a ratio method, a cutting method, a Q value method and/or a spectrum angle method to obtain the remote sensing image data after the interference is removed; the basic data generation device is used for carrying out mask technology processing and linear stretching on the remote sensing image data after the interference is removed to obtain basic remote sensing image data;

and/or the filter optimization device of the third device comprises a Q-value filtering module and a median filtering module; the Q value method filtering module is used for carrying out Q value method filtering on the abnormal information, and the median method filtering module is used for carrying out median method filtering processing on the image data subjected to Q value method filtering.

The invention has the following technical effects:

the invention provides a method for extracting mineralization and alteration remote sensing information, which comprises the steps of obtaining remote sensing image data, processing by using a mask technology to form basic data, selecting a research object, namely performing subsequent processing such as modeling and the like in a selected area, wherein the selected area belongs to a small range, namely establishing a successful abnormal extraction model of an ore deposit in the small range, sequentially utilizing normal distribution and a multiple linear regression method to respectively cut and check abnormal information, accurately obtaining parameters, mapping the space expansion of the parameters to new abnormal information extraction in the large range, obtaining a mineralization and alteration remote sensing abnormal image suitable for human eye observation, wherein the adopted change and the original change have different abnormal extraction modes due to different parameters, the successful experience is extended to the large range, the small range abnormal extraction is equivalent to modeling, and the large range is equivalent to the application of the model, the method is a comprehensive modeling and application method, and is a remote sensing ore finding information extraction technology which extracts the alteration remote sensing abnormality in a mode of mineralizing the alteration remote sensing abnormality space expansion and pertinently applies the result to ore deposits in different landscape areas. According to the method, the space of the alteration abnormal information is expanded, the weak information is enhanced, and the work of effectively detecting the weak signal and extracting the crossing alteration remote sensing abnormal in different landscape areas is realized; the method can solve the problems that the method for extracting the alteration remote sensing abnormity in different landscape areas is difficult, more false abnormity occurs, a large number of area type abnormity which is not related to ore finding is generated, and the ore finding has no pertinence; the mineralization alteration remote sensing abnormal space expansion and abnormal extraction technology related by the method has a small abnormal distribution range, reduces 'area type' abnormality which is not related to ore finding, has strong pertinence to detection of ore deposits in different landscape areas, is beneficial to more quickly and accurately selecting an ore finding target area, can play a role in saving time, manpower and material resources and achieving half the effort for mineral product detection work, and is a new technology for promoting production development by scientific and technical progress. By using the method, a plurality of mining (chemical) points are found in various landscape areas deeply cut in Yunnan, Guangdong vegetation coverage landscape areas, Guizhou karst landscape areas and the like, and the method makes a remarkable contribution to local mining finding.

The invention also relates to a device for extracting the mineralization and alteration remote sensing information, which corresponds to the method for extracting the mineralization and alteration remote sensing information and can also be understood as a device for realizing the method for extracting the mineralization and alteration remote sensing information, a first device, a second device and a third device which are connected in sequence are arranged, the devices work in a cooperative way, the second device can carry out specific processing modeling on basic remote sensing image data obtained by the first device in a selected area and extract abnormal information, cutting and checking on the abnormal information are carried out by utilizing a normal distribution method and a multiple linear regression method in sequence, an eigenvector meeting the checking is obtained by the third device and is used as an extraction parameter to carry out space expansion mapping on the eigenvector to the extraction of new abnormal information in a large range, and cutting and outputting new abnormal information, and finally obtaining a mineralized alteration remote sensing abnormal image suitable for human eye observation. The extracting device of the invention enhances the abnormal information, adopts series specific technologies, establishes an abnormal extracting model in a smaller area, and then expands the model into a larger space according to the same parameters, so that the extracted abnormal has good results, and practice proves that the extracting device also has very good effectiveness.

Drawings

FIG. 1 is a preferred flow chart of the method for extracting mineralization alteration remote sensing information.

FIG. 2 is another preferred flow chart of the method for extracting the mineralization alteration remote sensing information.

Fig. 3 is a comparison diagram of boundary information removal in preprocessing.

FIG. 4 is a comparison of before and after linear stretching.

Fig. 5 is a frequency distribution histogram.

Fig. 6 is a mineralization alteration remote sensing image finally output.

FIG. 7 is a block diagram of a preferred structure of a mineralization alteration remote sensing information extraction device of the present invention.

Detailed Description

The present invention will be described with reference to the accompanying drawings.

The invention relates to a method for extracting mineralization alteration remote sensing information, which comprises the following steps: the method comprises the following steps that firstly, remote sensing image data are obtained and processed by utilizing a mask technology to form basic data; secondly, performing histogram calculation processing on the basic data in a selected area to determine modeling data, extracting abnormal information from the modeling data by adopting a principal component analysis method, and respectively performing cutting and verification on the abnormal information by sequentially utilizing a normal distribution method and a multiple linear regression method; and a third step of obtaining the eigenvector meeting the check according to an eigenvector algorithm extracted from the abnormal information, taking the eigenvector as an extraction parameter to perform space expansion mapping to extract new abnormal information in a large range, and then performing cutting processing and outputting the new abnormal information. That is, in order to enhance the abnormal information, the invention establishes an abnormal extraction model in a small area, then expands the model into a larger space according to the same parameters, adopts the steps of mineralized alteration remote sensing abnormal space expansion and extraction, and realizes the operation of cross-over alteration remote sensing abnormal extraction in different landscape areas through modeling, so that the extracted abnormal has good results.

Preferably, a preferred flow chart of the mineralization alteration remote sensing information extraction method is shown in fig. 1. The method comprises the following steps: s1: acquiring remote sensing image data; the remote sensing image data is multiband remote sensing image data; s2: preprocessing the remote sensing image data to obtain basic remote sensing image data, wherein the preprocessing comprises boundary removing processing and interference removing processing and then processing by using a mask technology; and selecting the wave band of the remote sensing image data, and synthesizing a false color image. S1 and S2 are the first steps. S3: performing histogram calculation processing on the basic data in a selected small area to determine modeling data, then extracting abnormal information from the modeling data by adopting a principal component analysis method, cutting the abnormal information by utilizing normal distribution, comparing the cut data with the coincidence degree of sample coordinates by adopting an alteration abnormal center coordinate, obtaining a regression square sum and a residual square sum by utilizing a multiple linear regression method to measure the regression effect, and combining with an argument value test to realize the verification and evaluation of the abnormal information. And then obtaining the eigenvector meeting the check according to an eigenvector algorithm extracted by the abnormal information, and using the eigenvector as an extraction parameter to perform space expansion mapping to the extraction of new abnormal information in a large range. S3 combines the second step and the third step, is a successful abnormal extraction model of the ore deposit in a small range, obtains the parameters thereof, then expands the parameters to apply to the new abnormal extraction in a large range, because the change adopted by the different parameters and the original transformation have different abnormal extraction modes, the successful experience is extended to the large range, the abnormal extraction in the small range is equivalent to modeling, the large range is equivalent to the application of the model, so the abnormal extraction mode after the abnormal information extraction in the small region is combined with the large region space expansion is integrally formed, which can be understood as that an expanded abnormal extraction model is established, and the basic remote sensing image data is input to the pre-established expanded abnormal extraction model to obtain the abnormal information; wherein the abnormal information is vector information. The third step also includes S4-S7, S4: judging whether the abnormal information meets the abnormal characteristics or not, and if not, performing symbol conversion on the abnormal information; s5: performing exception segmentation to obtain segmented exception information; s6: filtering and optimizing the segmented abnormal information to obtain optimized abnormal information; s7: and synthesizing the optimized abnormal information and the synthesized false color image to obtain a mineralized alteration remote sensing abnormal image suitable for human eye observation.

The method for extracting the mineralization alteration remote sensing information is described in detail below with reference to another preferred flowchart shown in fig. 2.

In the first step, step S1: acquiring remote sensing image data; the remote sensing image data is multiband remote sensing image data; in this embodiment, remote sensing refers to a non-contact, remote sensing technique. Generally, the detection of the radiation and reflection characteristics of electromagnetic waves of an object by using a sensor/remote sensor is used. Remote sensing is to detect a target ground object and obtain information of reflected, radiated or scattered electromagnetic waves of the target ground object under the conditions of being far away from the target and being not in contact with the target by using instruments sensitive to the electromagnetic waves, such as a remote sensor. The remote sensing image data refers to electromagnetic wave information which is collected by an electromagnetic wave acquisition device carried in a carrier and recorded on a film; pictures acquired by the electromagnetic wave information in different wave bands are synthesized into multi-channel image data, and the image data is digitalized data.

Step S2, carrying out image data preprocessing on the remote sensing image data, and then utilizing mask technology processing and linear stretching to obtain basic remote sensing image data; and selecting the wave band of the remote sensing image data, and synthesizing a false color image.

Image data preprocessing refers to a series of data processing processes performed before image analysis in order to eliminate irrelevant images, recover useful real information, enhance the detectability of relevant information and simplify data to the maximum extent. Preferably, in this embodiment, the preprocessing includes a boundary removal processing and an interference removal processing, preferably, the boundary removal processing is to remove boundary information from the remote sensing image data of each waveband by combining a binary image processing technology, and preferably, the interference removal processing adopts a ratio method, a cutting method, a Q value method and/or a spectral angle method; after the preprocessing, the mask technology processing and the linear stretching are carried out to obtain the basic data (namely the basic remote sensing image data), and from another aspect, the processing before obtaining the basic data can be understood as the data preprocessing, and if so, the following preprocessing steps are carried out on the image, including: frame removal, interference removal, mask processing and linear stretching. Several pretreatment methods used in the present invention are described in detail below.

The frame is a boundary, and the frame information is boundary information. Coordinates (X, Y) of each wave band on a plane (representing the earth surface) are not coincident, and information mainly reflected on a boundary is not coincident, when remote sensing image data are acquired, the acquired data of each wave band are different, as shown by a symbol A in a comparison diagram shown in FIG. 3, if a researched area just contains boundary information, the accuracy of data processing is affected, and at the moment, the boundary information needs to be removed, so that each wave band contains effective information. The method adopted by the invention is that whether each wave band contains information is judged, if the information is contained, the value is assigned to 1, if the information is not contained, the value is assigned to 0, a binary image is generated, finally, the binary images generated by each wave band are multiplied, a new binary image is generated, and finally, the remote sensing data corresponding to each wave band is multiplied with the generated new binary image, so that the aim of removing boundary information is fulfilled. The specific formula is as follows:

wherein n refers to the total number of used remote sensing image wave bands, i is 1, …, n, x_iAnd y_iThe fingers refer to values before and after the i-band is removed. The image with the boundary information removed is represented by symbol B shown in fig. 3.

The interference refers to data which has an influence on image data analysis, and includes interference information such as noise information and shielding information, and nine types of common interference information such as clouds, water bodies, shadow areas, white mud lands, ice and snow, wetlands, dry channels and alluvial fans are often found in remote sensing image data. Since the general interference feature can have obvious characteristics such as cloud white and the like in both the 743 color composite image of the TM/ETM and the 631 color composite image of the ASTER, the interference can be judged by visual inspection under general conditions. The interference removal method includes a ratio method, a high-end or low-end cutting method, a Q value method, a spectrum angle method and the like.

The high-end or low-end cutting method is characterized in that interference ground objects have high reflection or strong absorption in a certain wave band on a remote sensing image, namely the interference ground objects in the certain wave band have high values or low values, for example, a water body has low values in the 7 th wave band of a TM/ETM, the low-end cutting method is adopted for processing, clouds have high values in the 1 st wave band of the TM/ETM, the high-end cutting method is adopted for processing, white mud fields have high values in the 3 rd wave band of the TM/ETM, the high-end cutting method is adopted for processing, and the like. The formula is as follows:

wherein i is 0, …, n, n refers to the total number of used remote sensing image wave bands, x_iAnd y_iThe fingers respectively refer to the wave band values before and after the interference information is removed from the i wave band, and b belongs to [1, …, n ∈]，C_bIs a constant number, x_bIs the value corresponding to the original b-band. The purpose of this formula is: given a constraint condition, images with this condition greater or less than a certain value remain, all others being assigned a value of zero.

The above ratio method is commonly used for removing various interferences such as shadow, water body, ice and snow, white mud land and the like, and firstly, the spectral characteristics of each wave band of the interfering ground object are judged, for example, the first wave band of the shadow area of the TM/ETM image is obviously larger than the 7 th wave band, so that a method that the 7 th wave band is larger than the first wave band is adopted, and a threshold value is set for removing. Or 5 th band to 4 th band or 4 th band to 3 rd band, etc. The formula is as follows:

wherein i is 0, …, n, n refers to the total number of used remote sensing image wave bands, x_iAnd y_iThe values of the bands before and after the removal of the 'sharp' information from the i band are respectively indicated, and a belongs to [1, …, n ∈]Ca is a constant, x_a，x_bAre the corresponding values of the original a and b bands. The purpose of this formula is: given a constraint condition, images with this condition greater or less than a certain value remain, all others being assigned a value of zero.

The Q value method mainly solves the interference of snow-side or lake-side wetlands, dry river channels, alluvial areas, thin clouds and the like. The Q value is defined as follows:

Q＝(x_a×k^a-x_b×k^b)/x_c×k^c

wherein x is_a，x_b，x_cFor the bands participating in principal component analysis, k_a，k_b，k_cX being involved in principal component variation_a，x_b，x_cCorresponding to the value of the eigenvector.

The spectral angle method is commonly used for removing interference with high difficulty such as thin cloud. The spectral angle method characterizes each multidimensional space point as a space vector, and compares the similarity of the space vector angles. This method is a supervised classification, requiring a known reference spectrum for each class. The reference spectrum can be stored in a reference spectrum library by ground measurement, or can be stored in the reference spectrum library by carrying out region-of-interest statistics from a picture unit with known conditions. The formula is as follows:

wherein (α) is the inner product of n-dimensional vector α, defined as the inner product

(α,β)＝α₁β₁+α₂β₂+…+α_nβ_n

When α is column vector (α) ═ α 'β + β' α

| α |, | β | are the lengths of vectors α, β,

the cos of the included angle can be obtained by obtaining the inner product and the length of α, and the included angle can be obtained by looking up the table.

The mask processing technique described above is used to determine which data needs to be taken into account and which do not. To generate a mask, a binary image is generated, where 0 represents data that does not need to participate in the calculation and 1 represents data that needs to participate in the calculation. The mask generation generally employs a band logical computation method.

τ is the generated mask, y_iTo remove data of "sharp" information, x_i，x_jAs the original data, it is the original data,is a relational operator (including<、≤、≯、>Not less than equal to or not equal to or equal to,is mathematical operator (including ±, ×, ÷ etc.), c₀，c₁，c₂Is a constant.

Our data are processed with a mask, our data a are processed with a mask τ:

X＝τ∩A

∩, is a masking algorithm, used to remove borders and interferences,

linear stretching: the masked data B is visually estimated by using a histogram graph, if the remote sensing image is an MXN window, a certain wave band pixel x of the remote sensing image in the window_j,k(j-1 … m; k-1 … n) interval is [ x₀，x_n]And counting the histogram in the window, wherein the formula is as follows:

where i ∈ [0, n ]]，x_j,k＝x_iIs a logical operation.

Let us take p_iI.e. max (p) and the minimum value of_i) And min (p)_i). Then take the minimum value as 0, the maximum value as 255, and the intermediate other values are resampled by interpolation. The formula is as follows:

wherein, y_j,kFor a certain band of pixels x of the original image_j,kValue after stretching, j ═ 1 … m; k is 1 … n; after the above processing, the basic data Y is obtained. A comparison of the before and after stretching is shown in FIG. 4.

In the second step and the third step, S3: inputting the basic remote sensing image data into a pre-established extended anomaly extraction model to obtain anomaly information; the extended anomaly extraction model is used for extracting anomaly information in the basic remote sensing image data; specifically, as shown in fig. 2, study object selection is performed first:

s31: and performing histogram calculation processing on the acquired basic remote sensing image data in a selected small area to determine modeling data, wherein the selected small area is a certain window area, specifically, the histogram calculation processing is performed through the skewness coefficient and the kurtosis coefficient of the pixel value in the window area for judgment, and if a preset condition is met, the image data to be modeled are obtained.

On the basis of remote sensing image data, an area M multiplied by N is assumed to exist, and a certain wave band pixel x of the remote sensing image in the area_j,k(j-1 … m; k-1 … n) interval is [ x₀，x_n]Within this window of statisticsThe formula is as follows:

wherein i ∈ [0, n ]]，x_j,kXi is a logical operation.

A certain waveband pixel value of the remote sensing image in the region is x_j,k(j is 1 … m, k is 1 … n), the mean value of the pixels is x, the standard deviation is sigma, and the histogram is judged by using the skewness coefficient and the kurtosis coefficient.

The skewness coefficient satisfies the formula:

wherein epsilon₁Given a very small positive number.

The kurtosis coefficient satisfies the formula:

wherein epsilon₂Given a very small positive number.

As shown in the frequency histogram distribution of fig. 5, if the histogram satisfies the skewness coefficient and the kurtosis coefficient that satisfy the preset condition, the M × N region is the image data to be modeled selected by the modeling.

S32: performing abnormal extraction on modeling data, namely performing abnormal extraction on the image data to be modeled by adopting a principal component analysis method to obtain mineralization and alteration abnormal information;

and extracting the abnormity of the acquired image data to be modeled by adopting a principal component analysis method to obtain mineralization and alteration abnormity information. The principal component analysis is a statistical method. A set of variables with possible correlation is converted into a set of linearly uncorrelated variables through orthogonal transformation. I.e. the data is transformed from one space to another space by some kind of linear transformation, and the basis vectors in the space are mutually perpendicular.

The original data with several bands is mapped onto several new principal components, each of which is a linear additive combination of eigenvectors, assuming p new variables ξ₁，ξ₂，……，ξ_pSo that the p new variables are linear functions of the original variable X and are uncorrelated with each other, i.e.

In practice, p is determined²A constant L_ik(i, k ═ 1, …, p) is expressed in a matrix:

CL＝λL (3)

the following formulas (2) and (3) show that: l may be referred to as a transformation matrix of equation (2) and may also be referred to as an eigenvector of equation (3), with each Lik being a component of this eigenvector; λ is the eigenvalues of the covariance matrix C. L and L have the following characteristics:

called traces, or gross variation, L (i.e., principal components) corresponding to different λ are linearly uncorrelated; and are orthogonal. From linear algebra, the eigen-polynomial of the covariance matrix C is known as det (λ I-C), and the root λ of the eigen-polynomial is the eigenvalue of the covariance matrix C. The eigenmatrix L is calculated as follows:

solving the covariance matrix C

Determining the eigenvalue lambda

|λI|-C＝0

Finding eigenvectors L

(λI-C)L＝0

As is known from the prior art, the eigenvector corresponding to the characteristic of the altered anomaly, typically the 4 th vector, is considered in correspondence with each band participating in the principal component analysis. And considering whether each component of the fourth eigenvector meets the characteristics corresponding to the alteration abnormal information, and if not, performing coincidence transformation on the fourth eigenvector. For example, table 1 below:

TABLE 1

If the component of the eigenvector corresponding to a certain abnormal information is characterized by V_a4>V_b4<V_c4>V_d4Then, V_a4、V_c4A constant and V_b4、V_d4Are of opposite sign, and V_a4And V_c4、V_b4And V_d4Are the same as the symbols in (a). If eigenvector 4 for remote sensing cutting of altered lesions requires V_c4Is positive and the calculation result is negative, it needs to be converted into positive, and the formula is as follows:

wherein,is a V_c4Results after fitting the transformation.

And (3) substituting the eigenvector 4 which is subjected to symbol conversion and corresponds to the abnormal information into the formula (2) to obtain mineralization and alteration abnormal information, wherein the abnormal information is vector information.

S33: and (3) abnormal optimization and verification, namely cutting and verifying abnormal information by sequentially utilizing a normal distribution and a multiple linear regression method, specifically, cutting the abnormal information by utilizing the normal distribution, comparing the cut data with the coincidence degree of the coordinate of a sample by using the change abnormal center coordinate, measuring the regression effect by utilizing the multiple linear regression method to obtain the regression square sum and the residual square sum, and realizing the verification and evaluation of the abnormal information by combining with the independent variable value test.

Before principal component analysis, the histogram of each waveband is in normal distribution after processing, the abnormal principal component histogram after conversion is also in normal distribution, and the abnormal segmentation is carried out by utilizing the theory related to normal distribution.

The normal distribution formula is as follows:

where X is a random variable and σ is referred to as the standard error. For multivariate analysis of principal component analysis, σ is called the standard deviation, defined as follows:

n is the number of samples and n is the number of samples,is a mean value, x_iIs the value of each sample. The characterization of sigma can be used in abnormal cutting or data cuttingThe scale of the state distribution curve. For example, the principal component analysis result may interpret the mean (X) as representing the background of the region, determine the lower abnormality limit using (X + k σ), and classify the abnormality intensity level. Typically, a minimum and maximum limit of ± 4 σ is taken.

When cutting abnormity, the subjective arbitrariness can be reduced by the scale, and the abnormity grading is calculated according to the formula:

l127.5 + k SF; or L127.5 + k 127.5/4; h + L1

H, L are cut high and low threshold values, respectively; k is a multiple; σ is the standard deviation; SK is a scale factor; SF and SK are given by the principal component analysis report.

Comparing the coincidence degree of the data X after abnormal segmentation and the coordinates of the center of the altered abnormal sample Y, and if the coincidence degree is larger than a given value epsilon, defining Y as a given check variable (mineral point or known abnormal point), X₁,x₂,…,x_nFor n independent variables (corresponding changed data X), a total of m observations are made, assuming first that there is a linear relationship between y and n independent variables:

y＝a₀+a₁×x₁+a₂x₂+…+a_nx_n+ε

in the formula, a₀,a₁,a₂,…,a_nIs a regression coefficient, is a constant, and represents that x is constant under the condition that other independent variables are not changed_j(j ═ 1,2, … n) by the average change amount per unit, and ε is the random error after removing the influence of n arguments on y, and the above equation is called a multiple linear regression model. Multiple linear regression with the conditions of (1) y and x₁,x₂,…,x_nHave a linear relationship therebetween; (2) each observation yj (j ═ 1,2, …, m) is independent of the others; (3) ε follows a normal distribution.

First using a₀+a₁×x₁+a₂x₂+…+a_nx_nTo estimate the mean value E (y) of y, assuming that ε obeys the mean valueIs 0 and variance is σ²Normal distribution of (i.e.,. epsilon. -N (0, sigma.))²) Then y obeys a mean value of E (y) and a variance of σ²Normal distribution of (i.e. y-N [ E (y), σ)²]Then m sets of sample observations:

x₁₁,x₁₂,…,x_1n,y₁

x₂₁,x₂₂,…,x_2n,y₂

.....................

x_m1,x_m2,…,x_mn,y_m

in the formula, x_ijDenotes x_jObserved value at i-th time. The following formula is provided:

the above formula is a mathematical model of n-element linear regression, in which a₀,a₁,a₂,…,a_nN +1 undetermined parameters, ε₁,ε₂,…,ε_mM random variables which are independent of each other and follow the same normal distribution. To simplify the representation, the matrix form is utilized:

the mathematical model of n element linear regression is

Y＝AX+E

Least squares estimation is performed according to the formula, first assuming b₀,b₁,b₂,…,b_nAre respectively n +1 returnsCoefficient of regression a₀,a₁,a₂,…,a_nThen the observed value is expressed as:

y_j＝b₀x_j1+b₁x_j2+…+b_nx_jn+e_j

e_jis an error e_jIs called residual, hypothesisIs y_jIs estimated, then,

in the above formula, j is 1,2, …, m. The residual ej represents the actual value y_jAnd the estimated valueThe degree of deviation. To make the estimated valueWith the actual value y_jAt best, the fit must be such that the sum of the squared residuals,

the minimum is reached, according to the higher mathematical principle, the extreme value is at 0, the equation is established,

the normal equation is derived from the above formula,

from the matrix X, the equations on both sides of the coefficients are represented by C and D, then,

then, the matrix form of the normal equation is

CB＝(X′X)B＝X′y＝D

B is an unknown vector, if the matrix coefficient C is full rank, the inverse matrix exists, the unknown vector B can be solved reversely,

B＝A^-1D＝(X′X)X′y

vector B is the optimization parameter.

The hypothesis testing and evaluation of the regression equation may be by dispersion analysis. The total variation is defined as the total variation,

SS is regression sum of squares, which is regression valueAnd mean valueThe sum of the squares of the differences reflects the fluctuation of Y caused by the change of the independent variable X, and the degree of freedom df_{Go back to}N (n is the number of independent variables).

MS is the sum of squares of the residuals and is the measured value y_jAnd the regression valueThe sum of squares of the differences, caused by experimental errors and other factors, is given a degree of freedom df_{Disabled person}＝m-n-1。

The total degree of freedom of variation is m-1.

If the observation value is given, the total variation is determined, the regression effect can be measured by using SS and MS, the larger the SS is, the more remarkable the regression effect is, and the larger the MS is, the poor regression effect is.

To examine the overall regression effect, dimensionless indices-determining coefficients R are defined²To indicate that the user is not in a normal position,

R²reflecting the proportion of contribution of regression dispersion to the total variation. R ═ R^1/2Called complex correlation coefficient, reflecting the degree of correlation of all independent variables with dependent variables. The larger the R2 and R values, the better the regression.

And performing hypothesis testing on each component of the abnormal alteration center coordinates of the cut abnormal information meeting the preset regression effect, and reserving the components meeting the testing conditions to obtain optimized mineralized abnormal alteration information.

The above is a global regression effect test, and cannot account for each independent variable x₁,x₂,…,x_nFor the dependent variable y, some independent variables may not act on the dependent variable or act on other independent variables to replace, so that the independent variables need to be removed from the regression equation, and each independent variable x is suggested_iWhether or not to displayLet us assume H0: a is_i＝0,i＝1,2,…n。

(1) F value test

The F test (F-test), the most commonly used alias name is called joint hypothesis test (joint hypothesis test), and is also called variance ratio test, variance homogeneity test. It is a test in which statistical values are subject to F-distribution under the null hypothesis (H0).

At H0: a is_iUnder the assumption that 0 is not the case,

for a given confidence α, look up the threshold value F corresponding to β from the F-value distribution table_βIf | F_i|〉F_βThe assumption of H0 was rejected, and the overall regression effect of the n independent variables was considered significant, whereas the overall regression effect was not significant.

(2) t test

the t test, also known as Student's t test, is mainly used for the sample with small content (such as n)<30) The overall standard deviation σ is an unknown normal distribution. the t test is to use the t distribution theory to deduce the probability of occurrence of difference, so as to compare whether the difference between two averages is significant or not. At H0: a is_iT test formula under the assumption of 0

For a given test level β, look up the threshold value t corresponding to β from the t-value distribution table_βIf | t_i|〉t_βReject hypothesis H0, consider a_iIf the difference is significant from the 0 value, the rejection should not be performed, otherwise, the rejection should be performed.

(3) p value test

Hypothesis testing is an important element in inferring statistics. The hypothesis test is performed by professional statistical software such as SAS, SPSS and the like, and a P Value (P-Value, Prohealth, Pr) is common in the hypothesis test and is another basis for performing test decision.

The P value, i.e., the probability, reflects the magnitude of the likelihood of an event occurring. Statistics the P values obtained by the significance test method generally show that P <0.05 is statistically different, P <0.01 is statistically different, and P <0.001 is very statistically different. Meaning that the probability that the difference between samples is due to sampling error is less than 0.05, 0.01, 0.001. In fact, the value of P cannot give any importance to the data, but only indicates the probability of an event. The statistical result shows that Pr > F, which can also be written as Pr (> F), P ═ P { F0.05> F } or P ═ P { F0.01> F }.

Assume H0: a is_i0, obeying to p-distribution statistics with degrees of freedom of 1 and m-n-1, respectively,

for a given test level β, the threshold value p can be found from the p-value distribution table_β(1, m-n-1) if p_i〉p_β(1, m-n-1), reject hypothesis H0, consider x_iThe y value is important and should not be removed, otherwise, it should be removed.

S34: extracting parameters

If the anomaly is satisfied with the above test, extracting the transformation coefficient, and acquiring the eigenvector according to the eigenvector algorithm extracted by the anomaly:

L＝[L₁...L_p]

the new parameter is a parameter of a place with a better abnormal effect, so that the new parameter can be used as a parameter for expanding an abnormal extraction range in a large-range space, and the consistency of mineral extraction is ensured.

S35: spatial extended exception extraction

Selecting several bands of data for the large range of data X maps onto several new principal components. The principal components are formed by linear addition and combination of eigenvectors. In mathematics, it is known to find some new variables x₁，x₂，……，x_pSo that they are linear functions of X, i.e.

In practice, p is determined²A constant L_ik(i, k ═ 1, …, p) is expressed in a matrix:

in the formula: l is an eigenmatrix, each L_ikIs a component of this eigenvector.

S4: new anomaly optimization

Carrying out symbol discrimination processing on the new abnormal eigenvector of each wave band, judging whether the abnormal information meets the abnormal characteristics, if not, carrying out symbol conversion on the abnormal information to obtain abnormal information of the symbol conversion;

the eigenvectors obtained are considered corresponding to the respective bands participating in the principal component analysis, and those eigenvectors corresponding to the abnormal characteristics of alteration, generally the 4 th vector, are considered. The correspondence is the same as table 1 described above. If an anomaly is characterized by V_a4>V_b4<V_c4>V_d4Then V_a4、V_c4A constant and V_b4、V_d4Are of opposite sign, and V_a4And V_c4、V_b4And V_d4Are the same as the symbols in (a). Eigenvector 4 requirement V for abnormal cleavage_c4Is positive, if negative, it needs to be converted into positive.

S5: novel abnormal dissection

The abnormal information meeting the conversion is subjected to abnormal segmentation according to the (X + k sigma) standard to obtain segmented abnormal information; wherein, X is a random variable representing abnormal information, k represents a scale factor, and sigma is standard deviation.

S6: filtering and optimizing the segmented abnormal information to obtain optimized abnormal information;

in order to prevent removing too much and damaging valuable information, after the exception extraction, preferably, further careful observation is carried out to see whether false exceptions caused by residual interference exist, and digital means are adopted to optimize the exceptions through post-processing. The post-treatment is preferably carried out by the Q-value method, Q being defined as Q (% 5 Xk)⁵-％7×k⁷)/％1×k¹

Where (% N) represents a pixel value in the TM nth band or database nth channel, such as% 1 represents a pixel value in the TM 1 st band or database 1 st channel,% 5 represents a pixel value in the TM 5 th band or database 5 th channel,% 7 represents a pixel value in the TM 7 th band or database 7 th channel; k is a radical ofⁿThe contribution coefficient (determined by PCA eigenvector) representing the n-th band of the TM, e.g. k¹Represents the contribution coefficient, k, of the TM 1 st band⁵Represents the contribution coefficient, k, of the TM band 5⁷Representing the contribution factor of TM band 7.

Median filtering is then performed to further optimize the anomalies. The median filtering is a nonlinear signal processing technology which is based on the ordering statistical theory and can effectively inhibit noise, and the basic principle of the median filtering is to replace the value of one point in a digital image or a digital sequence by the median of all point values in a neighborhood of the point, so that the surrounding pixel values are close to the true values, and isolated noise points are eliminated. The method is to sort the pixels in the plate according to the size of the pixel value by using a two-dimensional sliding template with a certain structure, and generate a monotonously ascending (or descending) two-dimensional data sequence. The two-dimensional median filter output is

g(x,y)＝med{f(x-k,y-l),(k,l∈W)}

Wherein f (x, y) and g (x, y) are respectively an original image and a processed image. W is a two-dimensional template, typically 3 × 3, 5 × 5 regions, and may also be of different shapes, such as lines, circles, crosses, circles, and the like.

S7: and synthesizing the optimized abnormal information and the synthesized false color image to obtain a mineralized alteration remote sensing abnormal image suitable for human eye observation. That is, for the base map, a false color map having a band combination in which the entropy of information is the largest is used, and vectors are represented by dot-line planes having the same projection. And carrying out superposition processing on the grids and the vectors by utilizing coordinate layering. Thereby forming an image suitable for the habit of the human eye.

The vector f (x, y, z), x, y are corresponding coordinates, z is a characteristic value, f (x, y, z) is a vector value, the grid g (x ', y'), x and y are corresponding coordinates, g (x ', y') is a grid gray value, and x ═ x 'y ═ y' is set, so that superposition of the grid g (x ', y') gray value and the vector g (x ', y') is realized.

Finally, a mineralization and alteration remote sensing abnormal image suitable for human eye observation is output, and a final image in a JPG or TIF format can be output. The final image as shown in fig. 6 can show remote sensing alteration abnormality of aluminum hydroxyl groups.

The invention also relates to a device for extracting the mineralization and alteration remote sensing information, which corresponds to the method for extracting the mineralization and alteration remote sensing information, and can also be understood as a device for realizing the method for extracting the mineralization and alteration remote sensing information, and a first device, a second device and a third device which are connected in sequence are arranged according to an optimal structural block diagram shown in fig. 7. Specifically, the first device acquires remote sensing image data and processes the remote sensing image data by using a mask technology to form basic data. Preferably, the first device comprises an image acquisition device, a preprocessing device, a basic data generation device and a band selection processing device connected with the image acquisition device, wherein the image acquisition device is used for acquiring original remote sensing image data; the remote sensing image data is multiband remote sensing image data; the image preprocessing device is used for preprocessing the acquired remote sensing image data, and the preprocessing comprises boundary removing processing and interference removing processing; the basic data generating device processes the preprocessed data by using a mask technology and linear stretching to form basic data; and the waveband selection processing device is used for carrying out waveband selection on the remote sensing image data and synthesizing a false color image. The image preprocessing device preferably comprises a boundary removing processing module and an interference removing processing module, wherein the boundary removing processing module is used for performing boundary removing processing on the original remote sensing images of all wave bands by combining a binary image processing technology to obtain boundary-removed remote sensing image data; and the interference removing processing module is used for removing interference on the remote sensing image data after the boundary is removed by adopting a ratio method, a cutting method, a Q value method and/or a spectrum angle method to obtain the remote sensing image data after the interference is removed.

And the second device is used for performing histogram calculation processing on the basic data in the selected area to determine modeling data, extracting abnormal information from the modeling data by adopting a principal component analysis method, and respectively performing cutting and verification on the abnormal information by sequentially utilizing a normal distribution method and a multiple linear regression method. The second device comprises a modeling device, an anomaly extraction device, an anomaly optimization device and an anomaly verification device which are sequentially connected, wherein the modeling device is connected with the basic data generation device of the first device and is used for performing histogram calculation processing on the basic data in a selected area to determine modeling data; the anomaly extraction device adopts a principal component analysis method to extract anomaly information of the modeling data; the abnormal optimization device cuts abnormal information by utilizing normal distribution; and the abnormal checking device is connected with the parameter extraction device of the third device and is used for comparing the coincidence degree of the secondary alteration abnormal center coordinates of the cut data and the sample coordinates, obtaining regression square sum and residual square sum by utilizing a multiple linear regression method to measure the regression effect and combining with the test of the independent variable value to realize the check and evaluation of the abnormal information.

And the third device is used for acquiring the eigenvector meeting the check according to an eigenvector algorithm for extracting the abnormal information, taking the eigenvector as an extraction parameter to perform space expansion mapping to extract the new abnormal information in a large range, and then performing cutting processing on the new abnormal information and outputting the new abnormal information. Preferably, the third device comprises a parameter extraction device, an extended anomaly extraction device, an anomaly cutting device, a filtering optimization device and a synthesis device which are connected in sequence, wherein the parameter extraction device obtains and extracts the eigenvectors meeting the verification according to an eigenvector algorithm extracted by the anomaly information; the extended anomaly extraction device performs space extension mapping on the parameters extracted by the parameter extraction device to extract new anomaly information in a large range; the abnormal cutting device cuts the new abnormal information; the filtering optimization device performs abnormal filtering optimization processing; and the synthesis device combines the abnormal filtering optimization processing result with the false color image output by the band selection processing device of the first device, superposes the grid and the vector by utilizing coordinate layering, and further outputs a mineralized alteration remote sensing abnormal image suitable for human eye observation. The filtering optimization device comprises a Q-value method filtering module and a median method filtering module; the Q value method filtering module is used for carrying out Q value method filtering on the abnormal information, and the median method filtering module is used for carrying out median method filtering processing on the image data subjected to Q value method filtering. Further preferably, the third device further includes a data determination device, as shown in fig. 7, the extended anomaly extraction device is connected to the anomaly segmentation device through the data determination device, the extended anomaly extraction device obtains eigenvectors of new anomalies, the data determination device performs symbol determination processing on the eigenvectors of the new anomalies of each band, and the anomaly segmentation device performs segmentation processing after the symbol determination processing.

It should be noted that the above-mentioned embodiments enable a person skilled in the art to more fully understand the invention, without restricting it in any way. Therefore, although the present invention has been described in detail with reference to the drawings and examples, it will be understood by those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention.

Claims (10)

1. A method for extracting mineralization alteration remote sensing information is characterized by comprising the following steps:

the method comprises the following steps that firstly, remote sensing image data are obtained and processed by utilizing a mask technology to form basic data;

secondly, performing histogram calculation processing on the basic data in a selected area to determine modeling data, extracting abnormal information from the modeling data by adopting a principal component analysis method, and respectively performing cutting and verification on the abnormal information by sequentially utilizing a normal distribution method and a multiple linear regression method;

and a third step of obtaining the eigenvector meeting the check according to an eigenvector algorithm extracted from the abnormal information, taking the eigenvector as an extraction parameter to perform space expansion mapping to extract new abnormal information in a large range, and then performing cutting processing and outputting the new abnormal information.

2. The method according to claim 1, wherein the remote sensing image data acquired in the first step is multiband remote sensing image data, and after the remote sensing image data is acquired, preprocessing is performed before basic data is formed by using mask technology processing, and the preprocessing comprises border removing processing and interference removing processing; carrying out wave band selection on the remote sensing image data to synthesize a false color image;

and in the third step, after new abnormal information is cut, abnormal filtering optimization is carried out, the grid and the vector are superposed by utilizing coordinate layering in combination with the false color image in the first step after optimization, and then a mineralized alteration remote sensing abnormal image suitable for human eye observation is output.

3. The method according to claim 2, wherein the second step is to cut the abnormal information by using normal distribution, compare the cut data with the coincidence degree of the sample coordinate by using the central coordinate of the altered abnormality, obtain the regression square sum and the residual square sum by using a multiple linear regression method to measure the regression effect, and realize the verification and evaluation of the abnormal information by combining with the test of the argument value.

4. The method according to claim 3, wherein the third step obtains eigenvectors satisfying the check according to an eigenvector algorithm for extracting abnormal information, and performs spatial expansion mapping to the new abnormal information in a large range as extraction parameters to obtain eigenvectors of new abnormal, and then performs symbol discrimination processing on the eigenvectors of new abnormal of each band, and performs cutting processing after the symbol discrimination processing.

5. The method according to one of claims 2 to 4, wherein the first step is to perform preprocessing after obtaining the remote sensing image data, then to form basic data by utilizing mask technology processing and linear stretching, the preprocessing boundary removing processing is to combine the remote sensing image data of each wave band with binary image processing technology to remove boundary information, and the preprocessing interference removing processing adopts a ratio method, a cutting method, a Q value method and/or a spectral angle method;

and/or, the abnormal filtering optimization in the third step adopts a Q value method and a median filtering method for filtering in turn.

6. A mineralization and alteration remote sensing information extraction device is characterized by comprising a first device, a second device and a third device which are connected in sequence,

the first device acquires remote sensing image data and processes the remote sensing image data by utilizing a mask technology to form basic data;

the second device performs histogram calculation processing on the basic data in a selected area to determine modeling data, extracts abnormal information from the modeling data by adopting a principal component analysis method, and cuts and checks the abnormal information by sequentially utilizing a normal distribution method and a multiple linear regression method;

and the third device obtains the eigenvector meeting the check according to an eigenvector algorithm for extracting the abnormal information, takes the eigenvector as an extraction parameter to perform space expansion mapping to extract new abnormal information in a large range, and then performs cutting processing and outputting of the new abnormal information.

7. The extraction device according to claim 6, wherein the first device comprises an image acquisition device, a preprocessing device, a basic data generation device and a band selection processing device connected with the image acquisition device, wherein the image acquisition device is used for acquiring original remote sensing image data; the remote sensing image data is multiband remote sensing image data; the image preprocessing device is used for preprocessing the acquired remote sensing image data, and the preprocessing comprises boundary removing processing and interference removing processing; the basic data generating device processes the preprocessed data by using a mask technology to form basic data; the band selection processing device is used for carrying out band selection on the remote sensing image data and synthesizing a false color image;

the third device comprises a parameter extraction device, an extended anomaly extraction device, an anomaly cutting device, a filtering optimization device and a synthesis device which are sequentially connected, wherein the parameter extraction device obtains and extracts the eigenvectors meeting the verification according to an eigenvector algorithm extracted by the anomaly information; the extended anomaly extraction device performs space extension mapping on the parameters extracted by the parameter extraction device to extract new anomaly information in a large range; the abnormal cutting device cuts the new abnormal information; the filtering optimization device performs abnormal filtering optimization processing; and the synthesis device combines the abnormal filtering optimization processing result with the false color image output by the band selection processing device of the first device, superposes the grid and the vector by utilizing coordinate layering, and further outputs a mineralized alteration remote sensing abnormal image suitable for human eye observation.

8. The extraction device according to claim 7, wherein the second device comprises a modeling device, an anomaly extraction device, an anomaly optimization device and an anomaly verification device which are connected in sequence, wherein the modeling device is connected with the basic data generation device of the first device and is used for performing histogram calculation processing on the basic data in a selected area to determine modeling data; the anomaly extraction device adopts a principal component analysis method to extract anomaly information of the modeling data; the abnormal optimization device cuts abnormal information by utilizing normal distribution; and the abnormal checking device is connected with the parameter extraction device of the third device and is used for comparing the coincidence degree of the secondary alteration abnormal center coordinates of the cut data and the sample coordinates, obtaining regression square sum and residual square sum by utilizing a multiple linear regression method to measure the regression effect and combining with the test of the independent variable value to realize the check and evaluation of the abnormal information.

9. The extracting apparatus according to claim 8, wherein the third means further comprises a data discriminating means, the extended anomaly extracting means is connected to the anomaly cutting means through the data discriminating means, the extended anomaly extracting means obtains eigenvectors of new anomalies, the data discriminating means performs sign discrimination processing on the eigenvectors of the new anomalies of each band, and the anomaly cutting means performs cutting processing after the sign discrimination processing.

10. The extraction apparatus according to one of claims 7 to 9, wherein the image preprocessing apparatus includes a de-boundary processing module and a de-interference processing module, and the de-boundary processing module is configured to perform de-boundary processing on the original remote sensing images of each wavelength band in combination with a binary image processing technique to obtain de-boundary remote sensing image data; the interference removing processing module is used for removing interference on the remote sensing image data after the boundary is removed by adopting a ratio method, a cutting method, a Q value method and/or a spectrum angle method to obtain the remote sensing image data after the interference is removed; the basic data generation device is used for carrying out mask technology processing and linear stretching on the remote sensing image data after the interference is removed to obtain basic remote sensing image data;

and/or the filter optimization device of the third device comprises a Q-value filtering module and a median filtering module; the Q value method filtering module is used for carrying out Q value method filtering on the abnormal information, and the median method filtering module is used for carrying out median method filtering processing on the image data subjected to Q value method filtering.