CN105405133A - Remote sensing image alteration detection method - Google Patents

Abstract

The invention discloses a method for detecting changes in remote sensing images, which includes: acquiring two-phase high-resolution optical remote sensing images X ₁ and X ₂ ; performing image registration on X ₁ and X ₂ ; using multivariate change detection methods for X ₁ and X ₂ Carry out radiation normalization correction; calculate the change vector amplitude X _M and spectral angle information X _SA according to X ₁ and X ₂ after _radiation normalization correction; calculate the optimal Segmentation threshold T; select a pseudo training sample area according to T and X _M ; combine X _M and X _SA as the input of the kernel FCM, and perform an optimal model parameter value on the kernel FCM combined with the spatial neighborhood information model according to the pseudo training sample area Selection: According to the selected optimal model parameter value, the method of combining kernel FCM with spatial neighborhood information is used to determine the changing area and non-changing area of the optical remote sensing image. The invention is more stable and has higher precision.

Description

A Method of Remote Sensing Image Change Detection

technical field

The invention relates to the technical field of remote sensing image change detection, in particular to a remote sensing image change detection method.

Background technique

With the continuous accumulation of multi-temporal high-resolution remote sensing data and the successive establishment of spatial databases, how to extract and detect change information from these remote sensing data has become an important research topic in remote sensing science and geographic information science. According to remote sensing images of different time phases in the same area, dynamic information such as cities and environments can be extracted to provide scientific decision-making basis for resource management and planning, environmental protection and other departments. my country's "Twelfth Five-Year Plan" will increase and expand the implementation of high-resolution earth observation projects that have been launched during the "Eleventh Five-Year Plan", focusing on basic theories including high-resolution remote sensing target and space environment feature analysis and high-reliability automatic interpretation And key technology research is becoming the focus of research to solve the major needs of national security and social and economic development.

Change detection of remote sensing images is to quantitatively analyze and determine the characteristics and process of surface changes from remote sensing data of different periods. Scholars from various countries have proposed many effective detection algorithms from different angles and application research, such as Change Vector Analysis (ChangeVectorAnalysis, CVA), clustering method based on Fuzzy C-means (FCM), etc. Among them, the traditional FCM-based multi-temporal optical remote sensing change detection usually performs CVA transformation first, and then performs FCM clustering on the magnitude of the change vector, and then obtains the change detection result. In this type of technology, the disadvantage of using FCM is that it is only suitable for spherical or ellipsoidal clustering, and it is extremely sensitive to noise and its outlier (Outlier). In addition, only using the magnitude of the change vector makes the original multispectral information not fully mined, which is not robust enough and the accuracy is not high.

In response to the above problems, many scholars have tried to solve them by adding different spatial neighborhood constraints to the FCM objective function, but the complexity of the high-resolution image detection environment and the lack of prior information of the target lead to certain limitations in these algorithms. limitations, the accuracy is not high. Therefore, it is necessary to study new high-resolution visible light remote sensing image change detection technology to effectively overcome the above difficulties.

Contents of the invention

The technical problem to be solved by the present invention is to provide a remote sensing image change detection method, which is a multi-temporal remote sensing image change detection method combined with the adaptive kernel FCM of CVA and SAM. The change detection result of the present invention is more robust and accurate. higher.

In order to solve the above technical problems, the present invention provides a remote sensing image change detection method, including:

Acquire two-temporal high-resolution optical remote sensing images X ₁ and X ₂ ;

Perform image registration on optical remote sensing images X ₁ and X ₂ ;

Perform radiometric normalization correction on optical remote sensing images X ₁ and X ₂ using multivariate change detection method;

Calculate the change vector magnitude X _M and spectral angle information X _SA respectively according to the optical remote sensing images X ₁ and X ₂ corrected by radiation normalization;

According to the change vector magnitude X _M , the optimal segmentation threshold T is calculated by using the Bayes principle and the maximum expectation algorithm;

Select the pseudo training sample area according to the optimal segmentation threshold T and the change vector magnitude X _M ;

Combining X _M and X _SA as the input of kernel FCM, according to the pseudo-training sample area, the kernel FCM is combined with the spatial neighborhood information model to select the optimal model parameter value;

According to the selected optimal model parameter values, the changing area and non-changing area of the optical remote sensing image are determined by using kernel FCM combined with spatial neighborhood information.

Implementing the present invention has the following beneficial effects: the present invention combines the change vector magnitude of multi-temporal remote sensing images and the spectral angle map (SpectralAngleMapper, SAM) of multi-temporal phases as the input of kernel FCM, and then based on the kernel FCM combined with the spatial neighborhood information method to get the final change detection result. Among them, the kernel parameters in the kernel FCM objective function are selected through pseudo training samples obtained based on CVA technology, and the change detection results are more robust and have higher precision.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without creative work.

Fig. 1 is a schematic flow chart of an embodiment of a remote sensing image change detection method provided by the present invention;

Figure 2 is the original high-resolution optical remote sensing image;

Fig. 3 is the comparison chart of the experimental results of the inventive method and other methods

detailed description

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

Fig. 1 is a schematic flow chart of an embodiment of the remote sensing image change detection method provided by the present invention. The present invention is a multi-temporal remote sensing image change detection method, which is mainly applicable to high-resolution optical remote sensing images. As shown in Fig. 1, the present invention The invention includes the steps of:

S101. Acquire two-temporal high-resolution optical remote sensing images X ₁ and X ₂ .

Among them, X ₁ and X ₂ are two high-resolution optical remote sensing images of the same area with different time phases.

_S102 . Perform image registration _on the optical remote sensing images X1 and X2.

Specifically, step S102 specifically includes steps:

S1021. Using ENVI14.8 remote sensing software to perform rough geometric correction on the optical remote sensing images X ₁ and X ₂ .

The specific operation steps of geometric coarse correction are: (1) display the reference image and the image to be corrected; (2) collect ground control points GCPs; GCPs should be evenly distributed in the whole image, and the number of GCPs should be at least greater than or equal to 9; Error; (4) choose a polynomial model; (5) use bilinear interpolation for resampling output. Among them, the bilinear difference method is: If we want to find the value of the unknown function f at point P=(x,y), assuming that we know the function f at Q ₁₁ =(x ₁ ,y ₁ ), Q ₁₂ =(x ₁ , y ₂ ), Q ₂₁ =(x ₂ , y ₁ ), and Q ₂₂ =(x ₂ , y ₂ ) the values of four points. If you choose a coordinate system so that the coordinates of these four points are (0,0), (0,1), (1,0) and (1,1), then the bilinear interpolation formula can be expressed as:

f(x,y)≈f(0,0)(1-x)(1-y)+f(1,0)x(1-y)+f(0,1)(1-x)y+ f(1,1)xy.

S1022. Perform geometric fine correction on X ₁ and X ₂ after geometric rough correction by using automatic matching and triangulation method.

Among them, the triangulation method is to use the point-by-point insertion method to construct the Delaunay triangulation network, and for each triangle, use the row and column numbers of its three vertices and the geographic coordinates of the corresponding point of the same name in the reference image to determine the affine transformation inside the triangle The model parameters are used to correct the image to be corrected to obtain the corrected remote sensing image.

S103. Using a multivariate change detection method (MultivariateAlterationDetection, MAD) to perform radiation normalization correction on the optical remote sensing images X ₁ and X ₂ .

Specifically, step S103 specifically includes steps:

S1031. Obtain a linear combination of brightness values in each band of the optical remote sensing images X ₁ and X ₂ to obtain a difference image with enhanced change information;

S1032. Determine a changed area and an unchanged area by thresholding according to the difference image;

S1033. Complete the relative radiation correction through the mapping equation of the two-temporal pixel pair corresponding to the unchanged area.

S104. Calculate the change vector magnitude X _M and the spectral angle information X _SA respectively according to the optical remote sensing images X ₁ and X ₂ corrected by radiation normalization.

Specifically, step S104 includes steps:

S1041. Calculate and obtain the change vector magnitude X _M according to the optical remote sensing images X ₁ and X ₂ corrected by radiation normalization.

in, $x_m(i,j)=\sqrt{\underset{b=1}{\overset{B}{\Σ}}{\left(x_{1b}\right(i,j)-x_{2b}(i,j\left)\right)}^2},$ In the formula, B represents the number of bands of each temporal remote sensing image, (i, j) is the coordinates of the image, X _1b represents the b-band image of X ₁ , and X _2b represents the b-band image of X ₂ ;

S1042. Calculate the change vector magnitude X _M according to the optical remote sensing images X ₁ and X ₂ corrected by radiation normalization,

in, $x_{SA}(i,j)=arcco\mathrm{the\; s}\left(\underset{b=1}{\overset{B}{\Σ}}\right(x_{1b}\left(i,j\right)x_{2b}\left(i,j\right)\left)\sqrt{\underset{b=1}{\overset{B}{\Σ}}{x}_{1b}^2(i,j)}\sqrt{\underset{b=1}{\overset{B}{\Σ}}{x}_{2b}^2(i,j)}\right).$

S105. Calculate an optimal segmentation threshold T by using the Bayesian principle and an Expectation-Maximization (EM) algorithm according to the change vector magnitude X _M .

Specifically, step S105 specifically includes steps:

S1051. Estimate the mean value _{m n} and variance σ _n of the unchanged class ω _n on the XM image by using the maximum expectation algorithm, and the mean value m _c and variance of the changed class ω _c are σ _c , where,

${\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \{</span>\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}\frac{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; \{</span>\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}\frac{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \}</span>$

${<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \{</span>\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}\frac{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; \{</span>\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}\frac{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \}</span>$

${\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \{</span>\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}\frac{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; \{</span>\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}\frac{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \}</span>$

${<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \{</span>\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}\frac{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; \{</span>\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}\frac{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \}</span>$

In the formula, t represents the number of iterations, and the superscript t represents the value of the t-th iteration of the current content, for example, Indicates the value of m _n at the t+1 iteration, and other representations are similar, express The value at the t+1th iteration, $p^{t+1}\left({\mathrm{\&omega;}}_{\mathrm{no}}\right)=\frac{\underset{x(i,j)\&Element;x_m}{\mathrm{\&Sigma;}}\frac{p^t\left({\mathrm{\&omega;}}_{\mathrm{no}}\right)p^t\left(x\right(i,j|{\mathrm{\&omega;}}_{\mathrm{no}}\left)\right)}{p^t\left(x\right(i,j\left)\right)}}{IJ},p^{t+1}\left({\mathrm{\&omega;}}_c\right)=\frac{\underset{x(i,j)\&Element;x_m}{\mathrm{\&Sigma;}}\frac{p^t\left({\mathrm{\&omega;}}_c\right)p^t\left(x\right(i,j|{\mathrm{\&omega;}}_c\left)\right)}{p^t\left(x\right(i,j\left)\right)}}{IJ},$ I and J represent the number of rows and columns of the image, respectively, Denotes the Gaussian distribution of the unchanged class ω _n on the X _M image, Indicates the Gaussian distribution that the variation class ω _c obeys on the X _M image;

S1052, according to the Bayes minimum error criterion, solve the formula $({\mathrm{\&sigma;}}_{\mathrm{no}}^2-{\mathrm{\&sigma;}}_c^2)T^2+2(m_{\mathrm{no}}{\mathrm{\&sigma;}}_c^2-m_c{\mathrm{\&sigma;}}_{\mathrm{no}}^2)T+m_c^2{\mathrm{\&sigma;}}_{\mathrm{no}}^2-m_{\mathrm{no}}^2{\mathrm{\&sigma;}}_c^2-2{\mathrm{\&sigma;}}_{\mathrm{no}}^2{\mathrm{\&sigma;}}_c^{2}l\mathrm{no}\&lsqb;\frac{{\mathrm{\&sigma;}}_{c}p\left({\mathrm{\&omega;}}_{\mathrm{no}}\right)}{{\mathrm{\&sigma;}}_{\mathrm{no}}p\left({\mathrm{\&omega;}}_c\right)}\&rsqb;=0,$ Get the optimal segmentation threshold T.

S106. Select a pseudo-training sample area according to the optimal segmentation threshold T and the variation vector magnitude X _M .

Specifically, step S106 includes the steps of:

S1061. According to the optimal segmentation threshold T and the change vector magnitude X _M , select the unchanging pseudo-training set samples as

S1062, according to the optimal segmentation threshold T and the change vector magnitude X _M , select the pseudo-training set sample of the change class as where δ is 15% of the dynamic range of _XM .

S107. Taking the combination of X _M and X _SA as the input of the kernel FCM, and selecting an optimal model parameter value for the kernel FCM combined with the spatial neighborhood information model according to the pseudo training sample area.

Specifically, step S107 specifically includes steps:

S1071. Combining X _M and X _SA as the input of the kernel FCM, constructing the kernel FCM combined with the spatial neighborhood information model is: $J_m=\underset{j=1}{\overset{C}{\mathrm{\&Sigma;}}}\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_{jk}^m(1-K(x_{mS}\left(k\right),v_j\left)\right)+\mathrm{\&alpha;}\underset{j=1}{\overset{C}{\mathrm{\&Sigma;}}}\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_{jk}^m(1-K({\stackrel{\&OverBar;}{x}}_{mS}\left(k\right),v_j\left)\right),$

In the formula, C is the number of clusters, N is the total number of samples, Indicates the fuzzy membership degree of the kth sample to the jth cluster center, m is the weighted index of the membership degree, The parameter α controls the penalty effect, is the combination of the local mean image of X _M and the local mean image of X _SA , $K(x,\mathrm{the\; y})=\exp \{-\frac{{(x-\mathrm{the\; y})}^2}{g^2}\}.$

S1072. Set the value ranges of the parameter α and the kernel parameter g, and use the dummy training sample set to search for the values of α and g when the change index C _index is the smallest as the optimal model parameter values.

Wherein, the change index C _index =D _index /k _T , k _T represents the Kappa coefficient of the model parameters on the pseudo-training sample set, N _n (α, g) represents the number of non-changing pixels of the entire image obtained by minimizing the objective function when α and g are given; N _c (α ,g) indicates the number of changing pixels in the entire image when α and g are given; TN _n (α,g) indicates the number of non-changing pixels in the pseudo-training sample set when α and g are given; TN _c ( α,g) represents the number of changed pixels in the pseudo-training sample set when α and g are given.

S108. According to the selected optimal model parameter values, the changing area and the non-changing area of the optical remote sensing image are determined by using kernel FCM combined with spatial neighborhood information.

Specifically, step S108 specifically includes:

S1081. Set the number of clusters C=2 in the kernel FCM combined with the spatial neighborhood information model, as the center of the initial unchanged class and the changed class, and select the vector corresponding to the minimum value and maximum value of the change vector amplitude X _M ; The weighted index m=2 of the degree of membership, ε is a constant greater than 0, and the values of parameter α and kernel parameter g are the selected optimal model parameter values;

S1082. Calculate the local window mean value of X _M and X _SA , and set the window size to 3×3;

S1083, adopted $u_{jk}=\frac{{\left(\right(1-K\left(x_{mS}\right(k),v_j))+\mathrm{\&alpha;}(1-K\left({\stackrel{\&OverBar;}{x}}_{mS}\right(k),v_j)\left)\right)}^{-1/(m-1)}}{\underset{j=1}{\overset{C}{\&Sigma;}}{\left(\right(1-K\left(x_{mS}\right(k),v_j))+\mathrm{\&alpha;}(1-K\left({\stackrel{\&OverBar;}{x}}_{mS}\right(k),v_j)\left)\right)}^{-1/(m-1)}}$ Update the fuzzy partition matrix;

S1084, adopted $v_j=\frac{\underset{k=1}{\overset{N}{\&Sigma;}}{u}_{jk}^m\left(K\right(x_{mS}\left(k\right),v_j)x_{mS}\left(k\right)+\mathrm{\&alpha;}K\left({\stackrel{\&OverBar;}{x}}_{mS}\right(k),v_j){\stackrel{\&OverBar;}{x}}_{mS}\left(k\right))}{\underset{k=1}{\overset{N}{\&Sigma;}}{u}_{jk}^m\left(K\right(x_{mS}\left(k\right),v_j)+\mathrm{\&alpha;}K\left({\stackrel{\&OverBar;}{x}}_{mS}\right(k),v_j))}$ Update the cluster center;

S1085. Repeatedly update the fuzzy partition matrix and cluster centers until the cluster centers of two adjacent iterations are smaller than ε;

S1086. Determine the final change detection map according to the fuzzy partition matrix u _jk , and obtain the changed area and the non-changed area of the optical remote sensing image.

Effect of the present invention can be further illustrated by following experimental results and analysis:

The experimental data of the present invention is multi-temporal SPOT high-resolution image data in the Littoral area of France, the image size is 400×400, and three wave bands B1, B2 and B3 are used. In order to verify the effectiveness of the present invention, the change detection method of the present invention is compared with the following change detection methods:

(1) CVA-based EM method (CVA-EM) [the detection method proposed in the article "Automatic analysis of difference image for unsupervised change detection" (IEEE Transactions on Geoscience and Remote Sensing, 2000, 38 (3): 1171-1182.) by Bruzzone L. et al. from Italy].

(2) Classification method of FCM combined with spatial neighborhood information (FCM-S) [Chensongchan et al. in the article "Robust Image Segmentation Using FCM With Spatial Constraints Based on New Kernel-Induced Distance Measure" (IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, 2004, 34(4): 1907-1916.) method mentioned in]

(3) The method of the present invention.

The detection performance is measured by four indicators: the number of false detections FP, the number of missed detections FN, the total number of errors OE and the Kappa coefficient. The closer FP, FN and OE are to 0, and the closer the Kappa coefficient is to 1, it indicates that the performance of the change detection method is better. The test results are shown in Table 1. It can be seen from Fig. 2, Fig. 3 and Table 1 that the performance of the detection method proposed by the present invention is better than the other two detection methods, which shows that the change detection method proposed by the present invention is effective.

Table 1 Comparison of multi-temporal SPOT5 image change detection results in Littoral area

method FP FN OE k CVA-EM 7919 3882 11801 0.705 FCM-S 1822 6928 8750 0.737 The method of the invention 2511 4689 7200 0.797 ideal 0 0 0 1

It should be noted that, in this document, the term "comprising", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.

In the several embodiments provided in this application, it should be understood that the disclosed method can be implemented in other ways. For example, the system embodiments described above are only illustrative, and professionals can further realize that, in combination with the units and algorithm steps of the examples described in the embodiments disclosed herein, electronic hardware, computer software, or two In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of functions in the above description. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present invention. Software modules can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other Any other known storage medium.

The above description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (8)

1. A method for detecting remote sensing image change is characterized by comprising the following steps:

obtaining two-time-phase high-resolution optical remote sensing image X₁And X₂；

To optical remote sensing image X₁And X₂Carrying out image registration;

optical remote sensing image X by using multivariate change detection method₁And X₂Carrying out radiation normalization correction;

optical remote sensing image X after normalization correction according to radiation₁And X₂Are respectively provided withCalculating the magnitude X of the variation vector_MAnd spectral angular information X_SA；

According to the magnitude X of the variation vector_MCalculating to obtain an optimal segmentation threshold value T by using a Bayes principle and a maximum expectation algorithm;

according to the optimal segmentation threshold value T and the variation vector amplitude value X_MSelecting a pseudo-training sample region;

mixing X_MAnd X_SACombining the input data as the kernel FCM, and selecting the optimal model parameter value of the kernel FCM combined with the spatial neighborhood information model according to the pseudo training sample region;

and determining a change region and a non-change region of the optical remote sensing image by adopting a method of combining kernel FCM with space neighborhood information according to the selected optimal model parameter value.

2. The method for detecting changes in remote-sensing images of claim 1, wherein the pair of optical remote-sensing images X₁And X₂Performing image registration, specifically comprising:

optical remote sensing image X by adopting ENVI14.8 remote sensing software₁And X₂Carrying out geometric rough correction;

roughly correcting the geometry of the X by automatic matching and triangulation₁And X₂And carrying out geometric fine correction.

3. A method for detecting changes in remote-sensing images as claimed in claim 1, characterized in that said optical remote-sensing image X is detected by a multivariate change detection method₁And X₂Performing radiation normalization correction, specifically comprising:

obtaining optical remote sensing image X₁And X₂Linearly combining the brightness values of all the wave bands to obtain a difference image with enhanced change information;

determining a changed area and an unchanged area according to the difference image through a threshold;

and finishing the relative radiation correction through a mapping equation of the two-time phase pixel corresponding to the unchanged area.

4. The method for detecting changes in remote-sensing images of claim 1, wherein the optical remote-sensing image X corrected by radiation normalization is used₁And X₂Respectively calculating the variation vector amplitude X_MAnd spectral angular information X_SAThe method specifically comprises the following steps:

optical remote sensing image X after normalization correction according to radiation₁And X₂Calculating to obtain the amplitude X of the variation vector_MWherein $X_M(i,j)=\sqrt{\underset{b=1}{\overset{B}{\&Sigma;}}{\left(X_{1b}\right(i,j)-X_{2b}(i,j\left)\right)}^2},$ wherein B represents the number of wave bands of each time phase remote sensing image, (i, j) is the coordinate of the image, X_1bRepresents X₁B band image of (1), X_2bRepresents X₂B-band images of (1);

optical remote sensing image X after normalization correction according to radiation₁And X₂Calculating to obtain the amplitude X of the variation vector_MWherein $X_{SA}(i,j)=arccos\left(\underset{b=1}{\overset{B}{\&Sigma;}}\right(X_{1b}\left(i,j\right)X_{2b}\left(i,j\right))/\sqrt{\underset{b=1}{\overset{B}{\&Sigma;}}{X}_{1b}^2(i,j)}\sqrt{\underset{b=1}{\overset{B}{\&Sigma;}}{X}_{2b}^2(i,j)}).$

5. the method for detecting changes in remote-sensing images of claim 1, wherein said vector magnitude X varies according to changes_MCalculating to obtain an optimal segmentation threshold value T by using a Bayes principle and a maximum expectation algorithm, and specifically comprising the following steps of:

estimating X using a maximum expectation algorithm_MUnchanged class omega on image_nMean value m of_nSum variance σ_nOf the variation class omega_cMean value m of_cThe sum variance is σ_cWherein

$m_n^{t+1}=\left\{\underset{X(i,j)\&Element;X_M}{\&Sigma;}\frac{p^t\left({\mathrm{\&omega;}}_n\right)p^t\left(X\right(i,j|{\mathrm{\&omega;}}_n\left)\right)}{p^t\left(X\right(i,j\left)\right)}X(i,j)\right\}/\left\{\underset{X(i,j)\&Element;X_M}{\&Sigma;}\frac{p^t\left({\mathrm{\&omega;}}_n\right)p^t\left(X\right(i,j|{\mathrm{\&omega;}}_n\left)\right)}{p^t\left(X\right(i,j\left)\right)}\right\}$

${\left({\mathrm{\&sigma;}}_n^2\right)}^{t+1}=\{\underset{X(i,j)\&Element;X_M}{\&Sigma;}\frac{p^t\left({\mathrm{\&omega;}}_n\right)p^t\left(X\right(i,j|{\mathrm{\&omega;}}_n\left)\right)}{p^t\left(X\right(i,j\left)\right)}\&lsqb;X(i,j)-m_n^t\&rsqb;\}/\left\{\underset{X(i,j)\&Element;X_M}{\&Sigma;}\frac{p^t\left({\mathrm{\&omega;}}_n\right)p^t\left(X\right(i,j|{\mathrm{\&omega;}}_n\left)\right)}{p^t\left(X\right(i,j\left)\right)}\right\}$

$m_c^{t+1}=\left\{\underset{X(i,j)\&Element;X_M}{\&Sigma;}\frac{p^t\left({\mathrm{\&omega;}}_c\right)p^t\left(X\right(i,j|{\mathrm{\&omega;}}_c\left)\right)}{p^t\left(X\right(i,j\left)\right)}X(i,j)\right\}/\left\{\underset{X(i,j)\&Element;X_M}{\&Sigma;}\frac{p^t\left({\mathrm{\&omega;}}_c\right)p^t\left(X\right(i,j|{\mathrm{\&omega;}}_c\left)\right)}{p^t\left(X\right(i,j\left)\right)}\right\}$

${\left({\mathrm{\&sigma;}}_c^2\right)}^{t+1}=\{\underset{X(i,j)\&Element;X_M}{\&Sigma;}\frac{p^t\left({\mathrm{\&omega;}}_c\right)p^t\left(X\right(i,j|{\mathrm{\&omega;}}_c\left)\right)}{p^t\left(X\right(i,j\left)\right)}\&lsqb;X(i,j)-m_c^t\&rsqb;\}/\left\{\underset{X(i,j)\&Element;X_M}{\&Sigma;}\frac{p^t\left({\mathrm{\&omega;}}_c\right)p^t\left(X\right(i,j|{\mathrm{\&omega;}}_c\left)\right)}{p^t\left(X\right(i,j\left)\right)}\right\}$

wherein t represents the number of iterations, t superscript represents the value at the t-th iteration of the current content,

$p^{t+1}\left({\mathrm{\&omega;}}_n\right)=\frac{\underset{X(i,j)\&Element;X_M}{\&Sigma;}\frac{p^t\left({\mathrm{\&omega;}}_n\right)p^t\left(X\right(i,j|{\mathrm{\&omega;}}_n\left)\right)}{p^t\left(X\right(i,j\left)\right)}}{IJ},p^{t+1}\left({\mathrm{\&omega;}}_c\right)=\frac{\underset{X(i,j)\&Element;X_M}{\&Sigma;}\frac{p^t\left({\mathrm{\&omega;}}_c\right)p^t\left(X\right(i,j|{\mathrm{\&omega;}}_c\left)\right)}{p^t\left(X\right(i,j\left)\right)}}{IJ},$

i and J represent the number of rows and columns of the image respectively,represents X_MUnchanged class omega on image_nThe gaussian distribution that is obeyed to,represents X_MVariation class omega on image_cA gaussian distribution obeyed;

solving a formula according to Bayes minimum error criterion $({\mathrm{\&sigma;}}_n^2-{\mathrm{\&sigma;}}_c^2)T^2+2(m_n{\mathrm{\&sigma;}}_c^2-m_c{\mathrm{\&sigma;}}_n^2)T+m_c^2{\mathrm{\&sigma;}}_n^2-m_n^2{\mathrm{\&sigma;}}_c^2-2{\mathrm{\&sigma;}}_n^2{\mathrm{\&sigma;}}_c^{2}ln\&lsqb;\frac{{\mathrm{\&sigma;}}_{c}p\left({\mathrm{\&omega;}}_n\right)}{{\mathrm{\&sigma;}}_{n}p\left({\mathrm{\&omega;}}_c\right)}\&rsqb;=0,$ And obtaining an optimal segmentation threshold value T.

6. The method for detecting changes in remote-sensing images of claim 1, wherein said determining is based on an optimal segmentation threshold T and a change vector magnitude X_MSelecting a pseudo-training sample region, specifically comprising:

according to the optimal segmentation threshold value T and the variation vector amplitude value X_MSelecting unchanged pseudo training set samples as

According to the optimal segmentation threshold value T and the variation vector amplitude value X_MSelecting a change-like pseudo-training setThe sample is

Wherein is X_M15% of the dynamic range.

7. The method for detecting changes in remote-sensing images of claim 1, wherein said detecting X is performed by a computer_MAnd X_SACombining the kernel FCM and the pseudo training sample region to be used as input of the kernel FCM, and performing optimal model parameter selection on the kernel FCM and the spatial neighborhood information model according to the pseudo training sample region, wherein the optimal model parameter selection specifically comprises the following steps:

mixing X_MAnd X_SAThe combination is used as the input of a kernel FCM, and the kernel FCM and space neighborhood information combination model is constructed as follows:

$J_m=\underset{j=1}{\overset{C}{\&Sigma;}}\underset{k=1}{\overset{N}{\&Sigma;}}{u}_{jk}^m(1-K(X_{MS}\left(k\right),v_j\left)\right)+\mathrm{\&alpha;}\underset{j=1}{\overset{C}{\&Sigma;}}\underset{k=1}{\overset{N}{\&Sigma;}}{u}_{jk}^m(1-K({\stackrel{\&OverBar;}{X}}_{MS}\left(k\right),v_j\left)\right),$ where C is the number of clusters, N is the total number of samples,representing the fuzzy membership of the kth sample to the jth class center, m being the weighted index of the membership, u_jk∈[0,1]And isThe parameter α controls the penalty effect,is X_MLocal mean image and X_SAThe local mean value image of (a) is combined,

setting the value ranges of the parameter α and the kernel parameter g, and searching the change index C by using a pseudo training sample set_indexα and g as the minimum value as the optimal model parameter value, wherein the change index C_index＝D_index/k_T，k_TKappa coefficient, N, representing model parameters on a pseudo-training sample set_n(α, g) shows minimizing the number of unchanged pixels of the entire image acquired using the objective function given α and g, N_c(α, g) shows the number of pixels that change for the entire image given α and g, TN_n(α, g) indicates the number of unchanged pixels in the pseudo-training sample set given α and g, TN_c(α, g) shows the number of changed pixels in the pseudo training sample set given α and g.

8. The method for detecting changes in remote-sensing images according to claim 7, wherein the determining of the changed area and the unchanged area of the high-resolution optical remote-sensing image by using a method of combining kernel FCM with spatial neighborhood information according to the selected optimal model parameter values specifically comprises:

setting the clustering number C of the kernel FCM in combination with the spatial neighborhood information model to be 2, taking the clustering number C as the center of the initial unchanged class and the initial changed class, and selecting and changing the vector amplitude X_MSetting a weighting index m of the membership degree to be 2, wherein the weighting index m is a constant greater than 0, and the values of the parameter α and the kernel parameter g are selected as the optimal model parameter values;

calculating X_M，X_SAThe window size is set to 3 × 3;

adopt the formula $u_{jk}=\frac{{\left(\right(1-K\left(X_{MS}\right(k),v_j))+\mathrm{\&alpha;}(1-K\left({\stackrel{\&OverBar;}{X}}_{MS}\right(k),v_j)\left)\right)}^{-1/(m-1)}}{\underset{j=1}{\overset{C}{\&Sigma;}}{\left(\right(1-K\left(X_{MS}\right(k),v_j))+\mathrm{\&alpha;}(1-K\left({\stackrel{\&OverBar;}{X}}_{MS}\right(k),v_j)\left)\right)}^{-1/(m-1)}}$ Updating the fuzzy partition matrix;

adopt the formula $v_j=\frac{\underset{k=1}{\overset{N}{\&Sigma;}}{u}_{jk}^m\left(K\right(X_{MS}\left(k\right),v_j)X_{MS}\left(k\right)+\mathrm{\&alpha;}K\left({\stackrel{\&OverBar;}{X}}_{MS}\right(k),v_j){\stackrel{\&OverBar;}{X}}_{MS}\left(k\right))}{\underset{k=1}{\overset{N}{\&Sigma;}}{u}_{jk}^m\left(K\right(X_{MS}\left(k\right),v_j)+\mathrm{\&alpha;}K({\stackrel{\&OverBar;}{X}}_{MS}\left(k\right),v_j\left)\right)}$ Updating the clustering center;

repeatedly updating the fuzzy partition matrix and the clustering center until the clustering center of two adjacent iterations is smaller than the threshold;

partition matrix u from the blur_jkAnd determining a final change detection image to obtain a change area and a non-change area of the optical remote sensing image.