A method, system, device and medium for processing non-uniform structure of distributed target images for Mars ionosphere detection SAR

Abstract

The present invention discloses a distributed target image non-uniform structure processing method, system, equipment and medium for Mars ionosphere detection SAR. The method comprises: preprocessing the original distributed target SAR image, calculating the gray level co-occurrence matrix according to the pixel value thereof, calculating the texture feature value of the window center position based on the gray level co-occurrence matrix, traversing the preprocessed distributed target SAR image to generate a texture feature image; segmenting the texture feature image by using the Otsu threshold algorithm to obtain an initial mask; then generating an image mask by using a region growing algorithm to obtain a mask image; calculating the Hadamard product between the mask image matrix and the preprocessed distributed target SAR image matrix to obtain a homogenized distributed target SAR image; the system, equipment and medium are used to implement the method; the present invention can not only remove non-uniform areas in a targeted manner, but also keep the phase of the homogenous area, and has the characteristic of high precision in removing non-uniform structure.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a method, system, device and medium for processing the non-uniform structure of distributed target images for Mars ionosphere detection SAR. Background Art

[0002] Spaceborne synthetic aperture radar (SAR) has been widely used in deep space exploration. Researchers can detect the ionosphere by observing the effect of radar echoes on the ionosphere, which is currently one of the important means of detecting the Martian ionosphere. Because the accuracy of radar echoes affects the precision of ionospheric detection, it is necessary to perform radiometric calibration of radar echoes. To address the difficulty of deploying manual calibrators on Mars, it is necessary to propose a radiometric calibration method for distributed target scenarios that is suitable for the unique Martian scene and has strong applicability. Homogenized target images are crucial for accurately obtaining antenna patterns and further radiometric calibration processing for spaceborne synthetic aperture radars using flat and uniform areas. Therefore, for non-uniform areas in SAR images, it is necessary to specifically remove non-uniform structures.

[0003] Currently, there are some studies on the treatment of non-uniform areas in SAR radar images at home and abroad. Masanobu Shimada et al. (M. Shimada, A. Freeman, A technique for measurement of spaceborne SAR antenna patterns using distributed targets [J]. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(1): 100-114) proposed a technique for measuring spaceborne SAR antenna patterns using distributed targets. The homogenization method based on the chi-square similarity test uses the chi-square similarity test to test the uniformity within a certain area size and remove the non-uniform parts. However, if the area size is too small, the non-uniform parts will not be removed, while if it is too large, the uniform parts will be removed. Zhuang Li (Zhuang Li. Research on spaceborne SAR radiation cross-calibration method based on natural interface targets [D]. Beijing University of Chemical Technology, 2022.) proposed a research on spaceborne SAR radiation cross-calibration method based on natural interface targets. The filter-based homogenization method uses the pixels around the pixel point to perform mean filtering, median filtering and other filtering methods, and uses filtering processing to smooth the non-uniform parts to increase the uniformity of the SAR radar image. However, it does not essentially remove the non-uniform part, and the non-uniform part still exists in the SAR radar image. Summary of the Invention

[0004] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a distributed target image non-uniform structure processing method, system, equipment and medium for Mars ionosphere detection SAR, which adopts the algorithm of gray-level co-occurrence matrix and texture eigenvalue. When there is a difference between the texture of the non-uniform structure and the texture of the uniform structure, the non-uniform structure can be removed, and the method has the characteristic of high precision in non-uniform structure removal.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A method for processing non-uniform structures of distributed target images for Mars ionosphere detection SAR includes the following steps:

[0007] Step 1: Distributed target SAR image preprocessing: input the original distributed target SAR image, perform Frost filtering to suppress the coherent speckle of the distributed target SAR image, and obtain the distributed target SAR image after coherent speckle suppression;

[0008] Step 2, texture feature image generation: Calculate the gray level co-occurrence matrix based on the pixel values ​​of the pre-processed distributed target SAR image. Based on the gray level co-occurrence matrix, calculate the texture feature value at the center of the window, traverse the pre-processed distributed target SAR image, and generate a texture feature image.

[0009] Step 3, mask generation: Based on the grayscale histogram of the texture feature image, the Otsu threshold algorithm is used to segment the foreground and background of the texture feature image to obtain an initial mask; then the region growing algorithm is used to generate an image mask to obtain a mask image;

[0010] Step 4, mask processing: Calculate the Hadamard product between the mask image matrix and the pre-processed distributed target SAR image matrix to obtain a homogenized distributed target SAR image.

[0011] The process of speckle suppression in step 1 is as follows:

[0012] The Frost filtering algorithm is used to suppress the coherent speckle of the input original distributed target SAR image. That is, according to formula (1) and formula (2), the four neighborhoods are used to traverse each pixel value in the image in turn to obtain the pixel gray value I after filtering. i ' j , the expression is as follows:

[0013] I′ ij =∑ p∈η m p I ij (1)

[0014]

[0015] Where η represents the four neighborhoods, i.e., the four pixels above, below, left, and right adjacent to the center pixel, I ij Represents the original distributed target SAR image pixel value, m p represents the weight value of the central pixel of the four neighborhoods, K represents the coordination factor, C s represents the pixel variance in the four neighborhoods, d s,p Represents the Euclidean distance between a single pixel in the four neighborhoods and the central pixel.

[0016] The process of generating the texture feature image in step 2 includes:

[0017] Step 2.1, set the window size:

[0018] Assume the window size is L*L, usually L is much smaller than n and is an odd number, the expression is as follows:

[0019]

[0020] Where w represents the window, L represents the number of rows and columns of the window;

[0021] Step 2.2: Mirror-extend the image edge of the distributed target SAR image after speckle suppression. Specifically:

[0022] Assume that the distributed target SAR image matrix I after speckle suppression is:

[0023]

[0024] Where S 11 represents the value at the (1, 1) position in the distributed target SAR image data, S nm Represents the value at the (n, m)th position in the image data;

[0025] Perform mirror extension on the distributed target SAR image data after speckle suppression to obtain the image edge:

[0026]

[0027] Where S ij (1≤i≤n,1≤j≤m) represents the data of the distributed target SAR image data after the image edge is mirror-extended, and I1 represents the distributed target SAR image matrix after the image edge is mirror-extended;

[0028] Step 2.3, read S ij Data with a window size of L*L in (1≤i≤n,1≤j≤m):

[0029]

[0030] Where, Represents the value at the (1, 1) position in the data of the distributed target SAR image after the image edge is extended by the mirror, Represents the value at the (L, L)th position in the data of the distributed target SAR image after the image edge is extended by the mirror;

[0031] Step 2.4, call the gray-level co-occurrence matrix function, calculate the gray-level co-occurrence matrix value corresponding to the center pixel of window w1 when the pixel pair spacing is 1 and the pixel pair position is 0°, 45°, 90°, and 135°, and normalize it to obtain the normalized gray-level co-occurrence matrix P', which is used as the gray-level co-occurrence matrix calculation value for texture feature extraction. The expression is as follows:

[0032]

[0033] Where, P 0° Represents the gray level co-occurrence matrix when the pixel position is 0°, P 45° Represents the gray level co-occurrence matrix when the pixel pair position is 45°, P 90° Represents the gray level co-occurrence matrix when the pixel pair position is 90°, P 135° Represents the gray level co-occurrence matrix when the pixel pair position is 135°, P′ 0° Represents the normalized gray-level co-occurrence matrix of the pixel pair at an angle of 0°, P′ 45° Represents the normalized gray-level co-occurrence matrix of pixel pairs at a 45° angle, P′ 90° Represents the normalized gray-level co-occurrence matrix of pixel pairs at a 90° angle, P′ 135° Represents the normalized gray-level co-occurrence matrix of pixel pairs at an angle of 135°;

[0034] Step 2.5, use the normalized gray-level co-occurrence matrix P' to calculate the angular second-order moment of the center pixel of the window as the eigenvalue of the center pixel of the L*L window. The expression is as follows:

[0035]

[0036] Where E represents the angular second-order moment value of the center pixel of the window;

[0037] Step 2.6, traverse the pre-processed distributed target SAR image and calculate the eigenvalue of each pixel:

[0038] Slide the L*L size window, and traverse each pixel in the preprocessed distributed target SAR image with the center pixel of the window. Calculate the eigenvalue of each pixel to obtain the texture feature image after edge mirror expansion. The expression is as follows:

[0039]

[0040] Where I2 represents the texture feature image matrix after edge mirror expansion;

[0041] In step 2.7, the texture feature image after edge mirror expansion is cropped to obtain the texture feature image of the distributed target SAR image after speckle suppression. The expression is as follows:

[0042]

[0043] Where I3 represents the texture feature image matrix of the distributed target SAR image after speckle suppression.

[0044] The process of generating the image mask in step 3 includes:

[0045] Step 3.1, perform Otsu threshold algorithm segmentation on the grayscale histogram of the texture feature image in step 2.7:

[0046] According to formula (11), the grayscale value of the image is traversed so that the inter-class variance σ of the foreground and background is 2 Maximum, the image grayscale value at this time is used as the image threshold t for segmentation, the expression is as follows;

[0047] σ 2 =w0(t)w1(t)[μ0(t)-μ1(t)] 2 (11)

[0048] Where w0 represents the ratio of the number of foreground pixels to the total number of pixels after threshold segmentation, w1 represents the ratio of the number of background pixels to the total number of pixels after threshold segmentation, μ0 represents the average value of the foreground pixels after threshold segmentation, and μ1 represents the average value of the background pixels after threshold segmentation.

[0049] Step 3.2, perform region growing on the segmented texture feature image, and select points with zero pixel values ​​in different regions of the segmented texture feature image as initial seed points I4(i,j);

[0050] Step 3.3: Compare the growth conditions of the initial seed point I4(i,j) with those of its eight adjacent points. If they meet the requirements, save the point to the growth region matrix. The growth conditions are as follows:

[0051] I4(i+u,j+v)-I4(i,j)≤0.15 (12)

[0052] Where I4(i,j) represents the pixel value of the seed point, and I4(i+u,j+v) represents the eight points adjacent to the seed point;

[0053] Step 3.4: Repeat step 3.3 for the remaining initial seed points until all initial seeds are traversed to obtain the image mask.

[0054] The process of mask processing in step 4 is as follows:

[0055] Assume that the size of the distributed target SAR image matrix after preprocessing is m*n. The size of the image mask matrix obtained after steps 2 and 3 is also m*n. According to formula (13), the Hadamard product of the distributed target SAR image matrix after preprocessing and the image mask matrix is ​​calculated to obtain the uniform distributed target SAR image S', which is expressed as follows:

[0056] S'=(I0⊙H) m*n (13)

[0057] Where I represents the original distributed target SAR image matrix, and H represents the image mask matrix.

[0058] The present invention also provides a distributed target image non-uniform structure processing system for Mars ionosphere detection SAR, comprising:

[0059] Image preprocessing module: used to perform speckle suppression on the original distributed target SAR radar image to obtain the preprocessed distributed target SAR image;

[0060] Image generation module: Based on the pixel values ​​of the pre-processed distributed target SAR image, the gray level co-occurrence matrix is ​​calculated. Based on the gray level co-occurrence matrix, the texture feature value at the center of the window is calculated. The pre-processed distributed target SAR image is traversed to generate a texture feature image.

[0061] Mask generation module: used to segment the texture feature image using the Otsu threshold algorithm, and then generate the image mask using the region growing algorithm to obtain the mask image;

[0062] Mask processing module: used to calculate the Hadamard product between the mask image matrix and the pre-processed distributed target SAR image matrix to obtain a uniform distributed target SAR image.

[0063] The present invention also provides a distributed target image non-uniform structure processing device for Mars ionosphere detection SAR, comprising:

[0064] Memory: a computer-readable device used to store the computer program of the above-mentioned method for processing the non-uniform structure of distributed target images for Mars ionosphere detection SAR;

[0065] Processor: used to implement the above-mentioned distributed target image non-uniform structure processing method for Mars ionosphere detection SAR when executing the computer program.

[0066] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the above-mentioned distributed target image non-uniform structure processing method for Mars ionosphere detection SAR.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] 1. The present invention uses the amplitude value of the radar signal and only operates on the intensity of the distributed target SAR image. It does not involve the processing of the radar phase value and has the characteristic of maintaining the radar data phase of a uniform scene.

[0069] 2. The present invention only uses the amplitude value of the radar signal and is independent of the specific characteristics of the target. It has strong adaptability and is suitable for SAR image homogenization in most scenarios.

[0070] 3. Compared with the traditional method that requires placing a manual calibrator at the calibration site to obtain calibration data to assist in subsequent processing, the present invention only uses distributed target SAR image data and does not require external manual calibrator equipment data supplement or support, making the method simpler and easier to implement in actual engineering applications.

[0071] 4. The present invention adopts the algorithm of gray-level co-occurrence matrix and texture eigenvalue, and when there is a difference between the texture of the non-uniform structure and the texture of the uniform structure, the non-uniform structure can be removed, and has the characteristic of high precision in removing the non-uniform structure.

[0072] 5. The four-step process of the present invention uses only matrix operations, which has the characteristics of high computational efficiency. It does not rely on equation solving and can be achieved only by matrix processing calculations.

[0073] 6. The present invention adopts the Otsu threshold algorithm. Some noise points will be generated in the image. The noise points are eliminated through the region growing algorithm, further improving the quality of the mask image.

[0074] In summary, compared with the prior art, the present invention, by adopting the gray-level co-occurrence matrix and texture eigenvalue algorithm, can remove the non-uniform structure when there is a difference between the texture of the non-uniform structure and the texture of the uniform structure, and has the characteristics of high precision in removing the non-uniform structure; by adopting the Otsu threshold algorithm and the region growing algorithm, noise points are eliminated, and the quality of the mask image is further improved; in addition, the present invention only operates on the intensity of the distributed target SAR image, and has the characteristics of maintaining the radar data phase of the uniform scene and strong adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a flow chart of the method of the present invention.

[0076] Figure 2is the original distributed target SAR image used in this invention.

[0077] Figure 3 This is the distributed target SAR image after speckle suppression generated in the present invention.

[0078] Figure 4 It is the texture feature value image generated in the present invention.

[0079] Figure 5 It is the initial mask generated by the Otsu threshold algorithm segmentation in the present invention.

[0080] Figure 6 It is the mask image after region growth generated in the present invention.

[0081] Figure 7 It is the uniformized distributed target SAR image in the present invention.

[0082] Figure 8 It is the original distributed target SAR image phase map in the present invention.

[0083] Figure 9 It is the phase diagram of the distributed target SAR image after homogenization in the present invention. DETAILED DESCRIPTION

[0084] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0085] See also Figure 1 A method for processing non-uniform structures of distributed target images for Mars ionosphere detection SAR includes the following steps:

[0086] Step 1, distributed target SAR image preprocessing: Input the original distributed target SAR image ( Figure 2 ), perform Frost filtering to suppress the coherent speckle of the distributed target SAR image, and obtain the distributed target SAR image after coherent speckle suppression, as shown in Figure 3 As shown, specifically:

[0087] The Frost filtering algorithm is used to suppress the coherent speckle of the input original distributed target SAR image. That is, according to equations (1) and (2), the four neighborhoods are used to traverse each pixel value in the image in turn to obtain the pixel gray value I′ after filtering. ij , the expression is as follows:

[0088] I′ ij =Σ p∈η m p I ij (1)

[0089]

[0090] Where η represents the four neighborhoods, i.e., the four pixels above, below, left, and right adjacent to the center pixel, I ij Represents the original distributed target SAR image pixel value, m p represents the weight value of the central pixel of the four neighborhoods, K represents the coordination factor, C s represents the pixel variance in the four neighborhoods, d s,p Represents the Euclidean distance between a single pixel in the four neighborhoods and the central pixel;

[0091] Step 2, texture feature image generation: calculate the gray level co-occurrence matrix of the pre-processed distributed target SAR image, calculate the texture eigenvalue, and generate the texture feature image, such as Figure 4 As shown, specifically:

[0092] Step 2.1, set the window size:

[0093] Assume the window size is L*L, usually L is much smaller than n and is an odd number, the expression is as follows:

[0094]

[0095] Where w represents the window, L represents the number of rows and columns of the window;

[0096] Step 2.2: Mirror-extend the image edge of the distributed target SAR image after speckle suppression. Specifically:

[0097] Assume that the distributed target SAR image data after speckle suppression is:

[0098]

[0099] Where S 11 represents the value at the (1, 1) position in the distributed target SAR image data, S nm represents the value at the (n, m)th position in the image data, and I represents the distributed target SAR image matrix after speckle suppression;

[0100] Perform mirror extension on the distributed target SAR image data after speckle suppression to obtain the image edge:

[0101]

[0102] Where S ij (1≤i≤n,1≤j≤m) represents the data of the distributed target SAR image data after the image edge is mirror-extended, and I1 represents the distributed target SAR image matrix after the image edge is mirror-extended;

[0103] Step 2.3, read S ijData with a window size of L*L in (1≤i≤n,1≤j≤m):

[0104]

[0105] Where, Represents the value at the (1, 1) position in the data of the distributed target SAR image after the image edge is extended by the mirror, Represents the value at the (L, L)th position in the data of the distributed target SAR image after the image edge is extended by the mirror;

[0106] Step 2.4, call the gray-level co-occurrence matrix function, calculate the gray-level co-occurrence matrix value corresponding to the center pixel of window w1 when the pixel pair spacing is 1 and the pixel pair position is 0°, 45°, 90°, and 135°, and normalize it to obtain the normalized gray-level co-occurrence matrix P', which is used as the gray-level co-occurrence matrix calculation value for texture feature extraction. The expression is as follows:

[0107]

[0108] Where, P 0° Represents the gray level co-occurrence matrix when the pixel position is 0°, P 45° Represents the gray level co-occurrence matrix when the pixel pair position is 45°, P 90° Represents the gray level co-occurrence matrix when the pixel pair position is 90°, P 135° Represents the gray level co-occurrence matrix when the pixel pair position is 135°, P′ 0° Represents the normalized gray-level co-occurrence matrix of the pixel pair at an angle of 0°, P4' 5° Represents the normalized gray-level co-occurrence matrix of pixel pairs at a 45° angle, P′ 90° Represents the normalized gray-level co-occurrence matrix of pixel pairs at a 90° angle, P′ 135° Represents the normalized gray-level co-occurrence matrix of pixel pairs at an angle of 135°;

[0109] Step 2.5, use the normalized gray-level co-occurrence matrix P' to calculate the angular second-order moment of the center pixel of the window as the eigenvalue of the center pixel of the L*L window. The expression is as follows:

[0110]

[0111] Where E represents the angular second-order moment value of the center pixel of the window;

[0112] Step 2.6, traverse the pre-processed distributed target SAR image and calculate the eigenvalue of each pixel:

[0113] Slide the L*L size window, and traverse each pixel in the preprocessed distributed target SAR image with the center pixel of the window. Calculate the eigenvalue of each pixel to obtain the texture feature image after edge mirror expansion. The expression is as follows:

[0114]

[0115] Where I2 represents the texture feature image matrix after edge mirror expansion;

[0116] In step 2.7, the texture feature image after edge mirror expansion is cropped to obtain the texture feature image of the distributed target SAR image after speckle suppression. The expression is as follows:

[0117]

[0118] Where I3 represents the texture feature image matrix of the distributed target SAR image after speckle suppression;

[0119] Step 3, mask generation: Based on the grayscale histogram of the texture feature image in step 2, the Otsu threshold algorithm is used to segment the image foreground and background to obtain the initial mask, such as Figure 5 As shown; the image mask is generated by using the region growing algorithm to obtain the mask image, as shown Figure 6 As shown, specifically:

[0120] Step 3.1, perform Otsu threshold algorithm segmentation on the grayscale histogram of the texture feature image in step 2.7:

[0121] According to formula (11), the grayscale value of the image is traversed so that the inter-class variance σ of the foreground and background is 2 Maximum, the image grayscale value at this time is used as the image threshold t for segmentation, the expression is as follows;

[0122] σ 2 =w0(t)w1(t)[μ0(t)-μ1(t)] 2 (11)

[0123] Where w0 represents the ratio of the number of foreground pixels to the total number of pixels after threshold segmentation, w1 represents the ratio of the number of background pixels to the total number of pixels after threshold segmentation, μ0 represents the average value of the foreground pixels after threshold segmentation, and μ1 represents the average value of the background pixels after threshold segmentation.

[0124] Step 3.2, perform region growing on the segmented texture feature image, and select points with zero pixel values ​​in different regions of the segmented texture feature image as initial seed points I4(i,j);

[0125] Step 3.3: Compare the growth conditions of the initial seed point I4(i,j) with those of its eight adjacent points. If they meet the requirements, save the point to the growth region matrix. The growth conditions are as follows:

[0126] I4(i+u,j+v)-I4(i,j)≤0.15 (12)

[0127] Where I4(i,j) represents the pixel value of the seed point, and I4(i+u,j+v) represents the eight points adjacent to the seed point;

[0128] Step 3.4: Repeat step 3.3 for the remaining initial seed points until all initial seeds are traversed to obtain the final region growing result, which is used as the generated image mask;

[0129] Step 4, mask processing: Calculate the Hadamard product of the generated image mask matrix and the pre-processed distributed target SAR image matrix to obtain a uniform distributed target SAR image, such as Figure 7 As shown, specifically:

[0130] Assume that the size of the distributed target SAR image matrix after preprocessing is m*n. The size of the image mask matrix obtained after steps 2 and 3 is also m*n. According to formula (13), the Hadamard product of the distributed target SAR image matrix after preprocessing and the image mask matrix is ​​calculated to obtain the uniform distributed target SAR image. The expression is as follows:

[0131] S'=(I0⊙H) m*n (13)

[0132] Where S' represents the homogenized distributed target SAR image, I represents the preprocessed distributed target SAR image matrix, and H represents the image mask matrix.

[0133] A distributed target image non-uniform structure processing system for Mars ionosphere detection SAR, comprising:

[0134] Image preprocessing module: used to perform speckle suppression on the original distributed target SAR radar image to obtain the preprocessed distributed target SAR image;

[0135] Image generation module: Based on the pixel values ​​of the pre-processed distributed target SAR image, the gray level co-occurrence matrix is ​​calculated. Based on the gray level co-occurrence matrix, the texture feature value at the center of the window is calculated. The pre-processed distributed target SAR image is traversed to generate a texture feature image.

[0136] Mask generation module: used to segment the texture feature image using the Otsu threshold algorithm, and then generate the image mask using the region growing algorithm to obtain the mask image;

[0137] Mask processing module: used to calculate the Hadamard product between the mask image matrix and the pre-processed distributed target SAR image matrix to obtain a uniform distributed target SAR image.

[0138] A distributed target image non-uniform structure processing device for Mars ionosphere detection SAR, comprising:

[0139] Memory: a computer-readable device used to store the computer program of the above-mentioned method for processing the non-uniform structure of distributed target images for Mars ionosphere detection SAR;

[0140] Processor: used to implement the above-mentioned distributed target image non-uniform structure processing method for Mars ionosphere detection SAR when executing the computer program.

[0141] A computer-readable storage medium stores a computer program, which, when executed by a processor, can implement the above-mentioned distributed target image non-uniform structure processing method for Mars ionosphere detection SAR.

[0142] Compared with the original distributed target SAR image ( Figure 2 ) compared with the uniform distributed target SAR image obtained by the processing method of the present invention ( Figure 7 ) It can be clearly seen that the non-uniform part (yellow light) in the original distributed target SAR image is effectively removed, which improves the uniformity of the distributed target SAR image.

[0143] Compared with the phase image of the original distributed target SAR image ( Figure 8 ), the uniform distributed target SAR image obtained by the processing method of the present invention ( Figure 9 ) phase diagram clearly shows that there is no phase change in the uniform area, indicating that through the processing method of the present invention, while the non-uniform area is targetedly eliminated, the phase of the uniform area can still be maintained, achieving the uniformity of the distributed target SAR image and greatly improving the uniformity of the image.

[0144] The coefficient of variation (CV) reflects the degree of data dispersion by measuring the ratio of the standard deviation to the mean. A smaller value indicates a more concentrated data distribution, meaning a higher uniformity. In image analysis, the coefficient of variation (CV) is often used to describe the uniformity of image pixel values, indirectly reflecting the image's quality or effect. A smaller CV indicates a higher uniformity, meaning a more concentrated distribution of pixel values. A larger CV indicates a less uniform distribution, meaning a more dispersed distribution of pixel values ​​within the image. The expression is as follows:

[0145]

[0146] In the formula, σ represents the standard deviation of the sample, and u represents the mean of the sample;

[0147] The coefficient of variation CV of the SAR image after image preprocessing is 107.27, and the coefficient of variation CV of the image after processing by the present invention is 73.48, indicating that the uniformity of the image processed by the present invention is improved.

[0148] The correlation coefficient r is often used to assess the similarity of data. Its value ranges from -1 to 1: 1 indicates a perfect positive correlation, 0 indicates no correlation, and -1 indicates a perfect negative correlation. The correlation coefficient can be used to quantitatively describe the degree of similarity between data. The expression is as follows:

[0149]

[0150] Where, X i represents the radar phase matrix element value after preprocessing, represents the average value of the radar phase matrix elements after preprocessing, Y i represents the radar phase matrix element value after processing by the present invention, represents the average value of the radar phase matrix elements after processing by the present invention;

[0151] The correlation coefficient between the radar phase after processing by the present invention and the radar phase after preprocessing is 0.98, indicating that the radar phase is well maintained.

Claims

1. A method for processing non-uniform structures of distributed target images for Mars ionosphere detection SAR, characterized in that: The following steps are involved: Step 1: Distributed target SAR image preprocessing: input the original distributed target SAR image, perform Frost filtering to suppress the coherent speckle of the distributed target SAR image, and obtain the distributed target SAR image after coherent speckle suppression; Step 2, texture feature image generation: Calculate the gray level co-occurrence matrix based on the pixel values ​​of the pre-processed distributed target SAR image. Based on the gray level co-occurrence matrix, calculate the texture feature value at the center of the window, traverse the pre-processed distributed target SAR image, and generate a texture feature image. Step 3, mask generation: Based on the grayscale histogram of the texture feature image, the Otsu threshold algorithm is used to segment the foreground and background of the texture feature image to obtain an initial mask; then the region growing algorithm is used to generate an image mask to obtain a mask image; Step 4, mask processing: Calculate the Hadamard product between the mask image matrix and the pre-processed distributed target SAR image matrix to obtain a homogenized distributed target SAR image.

2. The method for processing non-uniform structures of distributed target images for Mars ionosphere detection SAR according to claim 1, characterized in that: The process of speckle suppression in step 1 is as follows: The Frost filtering algorithm is used to suppress the coherent speckle of the input original distributed target SAR image. That is, according to equations (1) and (2), the four neighborhoods are used to traverse each pixel value in the image in turn to obtain the pixel gray value I′ after filtering. ij , the expression is as follows: I′ ij =∑ p∈η m p I ij (1) Where η represents the four neighborhoods, i.e., the four pixels above, below, left, and right adjacent to the center pixel, I ij Represents the original distributed target SAR image pixel value, m p represents the weight value of the central pixel of the four neighborhoods, K represents the coordination factor, C s represents the pixel variance in the four neighborhoods, d s,p Represents the Euclidean distance between a single pixel in the four neighborhoods and the central pixel.

3. The method for processing non-uniform structures of distributed target images for Mars ionosphere detection SAR according to claim 1, characterized in that: The process of generating the texture feature image in step 2 includes: Step 2.1, set the window size: Assume the window size is L*L, usually L is much smaller than n and is an odd number, the expression is as follows: Where w represents the window, L represents the number of rows and columns of the window; Step 2.2: Mirror-extend the image edge of the distributed target SAR image after speckle suppression. Specifically: Assume that the distributed target SAR image matrix I after speckle suppression is: Where S 11 represents the value at the (1, 1) position in the distributed target SAR image data, S nm Represents the value at the (n, m)th position in the image data; Perform mirror extension on the distributed target SAR image data after speckle suppression to obtain the image edge: Where S ij (1≤i≤n,1≤j≤m) represents the data of the distributed target SAR image data after the image edge is mirror-extended, and I1 represents the distributed target SAR image matrix after the image edge is mirror-extended; Step 2.3, read S ij Data with a window size of L*L in (1≤i≤n,1≤j≤m): Where, Represents the value at the (1, 1) position in the data of the distributed target SAR image after the image edge is extended by the mirror, Represents the value at the (L, L)th position in the data of the distributed target SAR image after the image edge is extended by the mirror; Step 2.4, call the gray-level co-occurrence matrix function, calculate the gray-level co-occurrence matrix value corresponding to the center pixel of window w1 when the pixel pair spacing is 1 and the pixel pair position is 0°, 45°, 90°, and 135°, and normalize it to obtain the normalized gray-level co-occurrence matrix P', which is used as the gray-level co-occurrence matrix calculation value for texture feature extraction. The expression is as follows: Where, P 0° Represents the gray level co-occurrence matrix when the pixel position is 0°, P 45° Represents the gray level co-occurrence matrix when the pixel pair position is 45°, P 90° Represents the gray level co-occurrence matrix when the pixel pair position is 90°, P 135° Represents the gray level co-occurrence matrix when the pixel pair position is 135°, P′ 0° Represents the normalized gray-level co-occurrence matrix of the pixel pair at an angle of 0°, P′ 45° Represents the normalized gray-level co-occurrence matrix of pixel pairs at a 45° angle, P′ 90° Represents the normalized gray-level co-occurrence matrix of pixel pairs at a 90° angle, P′ 135° Represents the normalized gray-level co-occurrence matrix of pixel pairs at an angle of 135°; Step 2.5, use the normalized gray-level co-occurrence matrix P' to calculate the angular second-order moment of the center pixel of the window as the eigenvalue of the center pixel of the L*L window. The expression is as follows: Where E represents the angular second-order moment value of the center pixel of the window; Step 2.6, traverse the pre-processed distributed target SAR image and calculate the eigenvalue of each pixel: Slide the L*L size window, and traverse each pixel in the preprocessed distributed target SAR image with the center pixel of the window. Calculate the eigenvalue of each pixel to obtain the texture feature image after edge mirror expansion. The expression is as follows: Where I2 represents the texture feature image matrix after edge mirror expansion; In step 2.7, the texture feature image after edge mirror expansion is cropped to obtain the texture feature image of the distributed target SAR image after speckle suppression. The expression is as follows: Where I3 represents the texture feature image matrix of the distributed target SAR image after speckle suppression.

4. The method for processing non-uniform structures of distributed target images for Mars ionosphere detection SAR according to claim 1, characterized in that: The process of generating the image mask in step 3 includes: Step 3.1, perform Otsu threshold algorithm segmentation on the grayscale histogram of the texture feature image in step 2.7: According to formula (11), the grayscale value of the image is traversed so that the inter-class variance σ of the foreground and background is 2 Maximum, the image grayscale value at this time is used as the image threshold t for segmentation, the expression is as follows; σ 2 =w0(t)w1(t)[μ0(t)-μ1(t)] 2 (11) Where w0 represents the ratio of the number of foreground pixels to the total number of pixels after threshold segmentation, w1 represents the ratio of the number of background pixels to the total number of pixels after threshold segmentation, μ0 represents the average value of the foreground pixels after threshold segmentation, and μ1 represents the average value of the background pixels after threshold segmentation. Step 3.2, perform region growing on the segmented texture feature image, and select points with zero pixel values ​​in different regions of the segmented texture feature image as initial seed points I4(i,j); Step 3.3: Compare the growth conditions of the initial seed point I4(i,j) with those of its eight adjacent points. If they meet the requirements, save the point to the growth region matrix. The growth conditions are as follows: I4(i+u,j+v)-I4(i,j)≤0.15 (12) Where I4(i,j) represents the pixel value of the seed point, and I4(i+u,j+v) represents the eight points adjacent to the seed point; Step 3.4: Repeat step 3.3 for the remaining initial seed points until all initial seeds are traversed to obtain the image mask.

5. The method for processing non-uniform structures of distributed target images for Mars ionosphere detection SAR according to claim 1, characterized in that: The process of mask processing in step 4 is as follows: Assume that the size of the distributed target SAR image matrix after preprocessing is m*n. The size of the image mask matrix obtained after steps 2 and 3 is also m*n. According to formula (13), the Hadamard product of the distributed target SAR image matrix after preprocessing and the image mask matrix is ​​calculated to obtain the uniform distributed target SAR image S', which is expressed as follows: S'=(I0⊙H) m*n (13) Where I represents the original distributed target SAR image matrix, and H represents the image mask matrix.

6. A distributed target image non-uniform structure processing system for Mars ionosphere detection SAR, characterized by: include: Image preprocessing module: used to perform speckle suppression on the original distributed target SAR radar image to obtain the preprocessed distributed target SAR image; Image generation module: Based on the pixel values ​​of the pre-processed distributed target SAR image, the gray level co-occurrence matrix is ​​calculated. Based on the gray level co-occurrence matrix, the texture feature value at the center of the window is calculated. The pre-processed distributed target SAR image is traversed to generate a texture feature image. Mask generation module: used to segment the texture feature image using the Otsu threshold algorithm, and then generate the image mask using the region growing algorithm to obtain the mask image; Mask processing module: used to calculate the Hadamard product between the mask image matrix and the pre-processed distributed target SAR image matrix to obtain a uniform distributed target SAR image.

7. A distributed target image non-uniform structure processing device for Mars ionosphere detection SAR, characterized in that: include: Memory: a computer-readable device for storing a computer program for a method for processing non-uniform structures of distributed target images for Mars ionosphere detection SAR according to any one of claims 1 to 5; Processor: used to implement the distributed target image non-uniform structure processing method for Mars ionosphere detection SAR as described in any one of claims 1-5 when executing the computer program.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, can implement the distributed target image non-uniform structure processing method for Mars ionosphere detection SAR as described in any one of claims 1 to 5.

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