Mars ionosphere detection SAR radiometric calibration method, system and device based on distributed target, medium and program product

Abstract

The invention belongs to the field of radar signal processing, and discloses a Mars ionosphere detection SAR radiometric calibration method, system and device based on a distributed target, a medium and a program product, and the method comprises the steps: employing Frost filtering processing, dividing intervals according to a pixel amplitude value, selecting a median as a cluster center value, traversing the square of a difference value between the pixel amplitude and the center value, and obtaining a Mars ionosphere detection SAR radiometric calibration result; dividing an interval to which the pixel belongs based on the minimum value to construct a mask image, and calculating the Hadamard product of the mask image and the filtered image to obtain a homogenized image; calculating the average power of each sampling point in the range direction along the azimuth direction, calculating the result of each sampling point in the antenna pattern along the range direction, and reconstructing the antenna pattern in the range direction by using least square fitting; a backscattering coefficient is calculated according to the reconstructed image and radar parameters, and radiometric calibration is completed; systems, devices, media are used for implementing the method; a program product includes a computer program implementing the method; the back scattering coefficient of the ground object target can be accurately acquired, and accurate radiation calibration of the Mars ionosphere detection SAR is realized.

Description

Technical Field

[0001] The present invention belongs to the field of radar signal processing technology, and in particular relates to a Mars ionosphere detection SAR radiation calibration method, system, equipment, medium and program product based on distributed targets. Background Art

[0002] Spaceborne synthetic aperture radar (SAR) echoes are affected by the ionosphere as they pass through the Martian atmosphere, causing distortion in the radar echo signal. By exploiting the ionospheric effect errors on the radar echo signal, parameters such as the total electron content, electron density, and ionospheric altitude can be inverted, enabling precise detection of the Martian ionosphere. However, achieving precise detection of the Martian ionosphere using spaceborne SAR echoes requires high amplitude and phase accuracy of the radar echoes, necessitating radiometric calibration of the radar echo data. Limited by current Mars exploration technology and the Martian environment, it is difficult to deploy corner reflectors on the Martian surface or select large forest areas as targets with stable scattering characteristics for radiometric calibration. Therefore, a distributed target-based radiometric calibration method for SAR radiometric detection of the Martian ionosphere is urgently needed, independent of artificial calibrators such as corner reflectors and applicable to the unique mountainous and desert-like geology of Mars.

[0003] At present, the research on the radiation calibration processing methods of spaceborne SAR at home and abroad mainly includes two categories: First, by deploying active or passive corner reflectors as strong point targets with known scattering characteristics in a predetermined calibration field, the radiation calibration processing is completed. For example, Li Liang et al. proposed an on-orbit radiation calibration test method based on polarization active calibrator for the Gaofen-3 satellite (Li Liang, Hong Jun, Chen Qi et al. On-orbit test analysis of Gaofen-3 SAR based on polarization active calibrator [J]. Journal of Electronics, 2018, 46(09): 2157-2164). Zhang Ting et al. analyzed and compared the performance of three types of passive corner reflectors: fan-shaped, square and triangular, and found that triangular corner reflectors are more suitable for SAR radiation calibration (Zhang Ting, Zhang Pengfei, Zeng Qiming. Research on corner reflectors in SAR calibration [J]. Remote Sensing Information, 2010, (03): 38-42, 70). However, due to the limitations of the current development of Mars exploration technology, it is difficult to deploy active or passive corner reflectors on the surface of Mars. Therefore, this type of radiation calibration method that relies on the deployment of calibrators is obviously difficult to ensure the radiation calibration processing of the Mars ionosphere sounding SAR; secondly, the radiation calibration is completed by using a large-area distributed target scene with stable and known scattering characteristics as a reference. Currently, distributed targets based on rainforest scenes are mostly used to complete the calibration processing. For example, Li et al. conducted a radiation calibration experiment on Sentinel-1 satellite data based on the Amazon rainforest (Li H., Mouche A., Stopa JE, et al. Calibration of the Normalized Radar Cross Section for Sentinel-1 Wave Mode[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(3): 1514-1522). To calibrate the radiation of the Mars exploration radar system, Castaldo et al. used the radar equation to derive the radiation calibration equation for the ice surface of the SHARAD exploration satellite launched by NASA, and focused on verifying the dielectric constant of the Argyle Planitia in the north pole of Mars (Castaldo L, Alberti G, Cirillo G, et al. Scientific calibration of SHARAD data over Martian surface[C]. 2013 Signal Processing Symposium (SPS), 2013: 1-5).However, the above-mentioned existing methods of using distributed target scenes to carry out radiation calibration processing are all limited by the unique mountainous and desert surface features of Mars. That is, the processing methods using rainforest scenes are difficult to implement, or the use of the polar ice cap regions of Mars for radiation calibration processing imposes strict restrictions on the orbit design of the SAR satellite itself, and it is difficult to achieve real-time radiation calibration processing for the entire scene. 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 Mars ionosphere sounding SAR radiation calibration method, system, equipment, medium and program product based on distributed targets. Through Frost filtering-based distributed target SAR image coherent speckle suppression processing, image homogenization processing, least squares fitting reconstruction of antenna radiation pattern and backscattering coefficient calculation, accurate radiation calibration of radar image data obtained by Mars ionosphere sounding SAR is achieved. It has the advantages of high processing accuracy, simple processing steps, no dependence on external calibrator and can cover the full-time radiation calibration processing on orbit.

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

[0006] A SAR radiation calibration method for Mars ionosphere detection based on distributed targets includes the following steps:

[0007] Step 1, speckle suppression processing: For the distributed target image acquired by the Mars ionosphere sounding SAR, Frost filtering is used to suppress the speckle effect of the distributed target image and obtain the distributed target image after speckle suppression processing;

[0008] Step 2: Image homogenization: Divide the distributed target image after speckle reduction into numerical intervals based on the pixel amplitude values. Select the median of each amplitude interval as the cluster center value. Calculate the squared difference between the amplitude of each pixel and the center value of each cluster in the distributed target image after speckle reduction. Use the minimum squared difference as the criterion for dividing the numerical intervals to which each pixel belongs. Construct a mask image based on the pixel numerical interval divisions. Calculate the Hadamard product between the mask image and the distributed target image after speckle reduction to obtain a homogenized target image.

[0009] Step 3, antenna pattern reconstruction: Calculate the average power of each sampling point in the range direction along the azimuth direction of the homogenized target image, calculate the antenna pattern results at each sampling point along the range direction based on the average power, use the least squares fitting method to process the antenna pattern results at each sampling point along the range direction, and obtain the range antenna pattern reconstruction result;

[0010] Step 4: Backscatter coefficient calculation: Based on the range antenna pattern reconstruction result and radar system parameters, the backscatter coefficient is calculated to complete the radiation calibration process.

[0011] The specific process of the speckle reduction process in step 1 is as follows:

[0012] The distributed target image S with a size of X*Y obtained by the Mars ionosphere detection SAR o , where X is the number of sampling points in the azimuth direction and Y is the number of sampling points in the range direction. Frost filtering is used to realize the distributed target image S o Coherent speckle suppression, obtain the distributed target image S after coherent speckle suppression processing F Specifically, according to formulas (1) and (2), we traverse the distributed target image S o Each pixel value S ij , the distributed target image S after speckle suppression is calculated F Corresponding pixel value result S i ' j , the expression is as follows:

[0013]

[0014] Where E represents the four adjacent areas consisting of the four pixels above, below, left and right adjacent to the corresponding pixel, m p represents the weighting coefficient of the corresponding pixel, K represents the coordination factor, D represents the variance of all pixel values ​​in the four adjacent areas, d p Represents the Euclidean distance between each pixel and the corresponding pixel in the four adjacent regions, 1≤i≤X, 1≤j≤Y.

[0015] The specific process of image homogenization in step 2 is as follows:

[0016] Step 2.1, calculate the distributed target image S after speckle suppression processing F Cluster center value of each pixel amplitude interval:

[0017] Set the number of pixel amplitude value intervals N, and calculate the distributed target image S after speckle suppression according to formula (3): F The cluster center value M of each pixel amplitude interval n :

[0018] M n =S min +(2n-1)·(S max -S min ) / 2N (3)

[0019] Where S max Represents the distributed target image S after speckle suppression processing F The maximum pixel amplitude, S min Represents the distributed target image S after speckle suppression processing FMinimum value of pixel amplitude, n=1,2,3,…,N;

[0020] Step 2.2, calculate the distributed target image S after speckle suppression processing F The square of the difference between each pixel amplitude and the center value of each cluster:

[0021] According to formula (4), the pixel amplitude value S at coordinate (i, j) is calculated i ' j and the center value M of each cluster n The square of the difference Q n :

[0022] Q n =∑(S i ' j -M n ) 2 (4)

[0023] Step 2.3: Determine the numerical range of each pixel:

[0024] According to the difference square result Q calculated in step 2.2 n , select the minimum value Q t , where 1≤t≤N, based on which the pixel's numerical interval is determined to be interval t;

[0025] The distributed target image S after traversal speckle suppression processing F Repeat steps 2.1 to 2.3 for all pixels in the image, and count the numerical intervals to which all pixels belong;

[0026] Step 2.4, construct the mask image:

[0027] Construct the distributed target image S after speckle suppression processing F Mask image M of the same size a Matrix, according to the pixel value interval division result obtained in step 2.3, the mask image M a The matrix is ​​assigned, and the assignment rules are as follows:

[0028] The coordinate positions of all pixels in the numerical interval t=N are in the mask image M. a The pixel amplitude in the matrix is ​​assigned a value of 0;

[0029] Mask image M a The pixel amplitudes of all other coordinate positions in the matrix are assigned a value of 1;

[0030] Step 2.5, obtain the homogenized target image:

[0031] According to formula (5), the distributed target image S after speckle suppression is calculated FWith the mask image M a The Hadamard product of the matrix is ​​used to obtain the homogenized target image matrix S H , the expression is as follows:

[0032] S H =(S F ⊙M a ) X*Y (5)

[0033] Where ⊙ represents the matrix Hadamard product operation, X is the number of sampling points in the azimuth direction, and Y is the number of sampling points in the range direction.

[0034] The specific process of antenna pattern reconstruction in step 3 is as follows:

[0035] Step 3.1, calculate the average power of each sampling point in the range:

[0036] According to formula (6), the average power of each sampling point in the distance direction is calculated along the azimuth direction of the homogenized target image.

[0037]

[0038] Where, Represents the homogenized target image matrix S H In the element, X is the number of sampling points in the azimuth direction, and Y is the number of sampling points in the distance direction;

[0039] Step 3.2, calculate the antenna pattern:

[0040] Average power at each sampling point according to distance The antenna pattern along the distance to each sampling point is calculated by equations (7) and (8) j (1≤j≤Y):

[0041]

[0042] G j =G j-temp / max(G1,G2,...,G Y ) (8)

[0043] Where R j Indicates the slant distance corresponding to each distance sampling point, G j-temp Indicates the intermediate result of antenna pattern calculation, max(·) indicates the maximum value operation;

[0044] Step 3.3, obtain the range antenna pattern reconstruction result:

[0045] According to the antenna pattern along the distance to each sampling point result G j , the least squares fitting method is used to obtain the range antenna pattern reconstruction result Gfinal .

[0046] The specific process of backscatter coefficient calculation in step 4 is as follows:

[0047] Reconstruct the antenna pattern G according to the distance obtained in step 3 final The backscatter coefficient σ is calculated by equation (9) with the radar system parameters:

[0048]

[0049] Where S o is the original SAR image of size X*Y, where X is the number of sampling points in azimuth, Y is the number of sampling points in range, L represents the radar signal transmission attenuation, R represents the slant range, λ represents the radar signal wavelength, and P t represents the radar system transmit power, ρ a represents the azimuth resolution, ρ r Indicates the range resolution, k a represents the azimuth gain, k r Indicates the range gain.

[0050] A Mars ionosphere detection SAR radiation calibration system based on distributed targets, comprising:

[0051] Coherent speckle suppression processing module: For the distributed target images obtained by the Mars ionosphere sounding SAR, Frost filtering is used to suppress the coherent speckle effect of the distributed target images and obtain the distributed target images after coherent speckle suppression processing;

[0052] Image homogenization processing module: divide the numerical intervals according to the pixel amplitude values ​​of each distributed target image after the coherent speckle suppression process, select the median of each amplitude numerical interval as the cluster center value, traverse and calculate the square difference between the amplitude of each pixel and the center value of each cluster in the distributed target image after the coherent speckle suppression process, and use the minimum value of the square difference as the criterion to divide the numerical interval to which each pixel belongs. Construct a mask image based on the numerical interval division of the pixel, and calculate the Hadamard product between the mask image and the distributed target image after the coherent speckle suppression process to obtain a homogenized target image;

[0053] Antenna pattern reconstruction module: Calculate the average power of each sampling point in the range direction along the azimuth direction of the homogenized target image, calculate the antenna pattern results at each sampling point along the range direction based on the average power, use the least squares fitting method to process the antenna pattern results at each sampling point along the range direction, and obtain the range antenna pattern reconstruction results;

[0054] Backscatter coefficient calculation module: Based on the range antenna pattern reconstruction results and radar system parameters, the backscatter coefficient is calculated to complete the radiation calibration process.

[0055] A SAR radiation calibration device for Mars ionosphere detection based on distributed targets, comprising:

[0056] Memory: used for storing a computer program for implementing a SAR radiation calibration method for Mars ionosphere detection based on distributed targets;

[0057] Processor: used to implement the Mars ionosphere detection SAR radiation calibration method based on distributed targets when executing the computer program.

[0058] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a SAR radiation calibration method for Mars ionosphere detection based on distributed targets.

[0059] A computer program product includes a computer program. When the computer program is executed by a processor, the computer program implements a Mars ionosphere detection SAR radiation calibration method based on distributed targets.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] 1. The present invention realizes radiation calibration by processing the amplitude values ​​of the distributed target images obtained by the Mars ionosphere sounding SAR, without involving phase processing operations, and has the characteristic of being able to well maintain the phase information of the distributed target images.

[0062] 2. The present invention realizes radiation calibration processing by adopting distributed target image data, without the need for deploying external manual calibrators or external reference data as support, and has the characteristics of being easy to implement in actual engineering applications.

[0063] 3. During the radiation calibration process, the present invention has no specific requirements for the scattering characteristics of the ground objects corresponding to the distributed target images, and does not require the scattering characteristics data of the ground objects as a reference. It can carry out radiation calibration processing for various types of ground object scenes such as Martian sand, mountains, and ice surfaces, and has the characteristics of strong scene applicability.

[0064] 4. The present invention comprehensively adopts coherent speckle suppression processing to reduce the influence of multiplicative noise, homogenization processing to screen the stable and uniform area of ​​scattering characteristics, least squares fitting processing to reconstruct the antenna radiation pattern and other operations to ensure the accurate inversion of the backscattering coefficient, and has the characteristics of high precision in the SAR radiation calibration processing of Mars ionosphere detection.

[0065] 5. The present invention only uses matrix data processing and a four-step processing flow to achieve distributed target image data radiation calibration processing, which has the characteristics of simple and feasible processing flow.

[0066] In summary, the present invention adopts operations such as speckle suppression processing to reduce the influence of multiplicative noise, homogenization processing to screen the stable and uniform area of ​​scattering characteristics, and least squares fitting processing to reconstruct the antenna radiation pattern to realize radiation calibration processing of distributed target image data. It does not require the deployment of external artificial calibrators or external reference data as support, does not involve phase processing operations, can well maintain the phase information of distributed target images, is easy to implement in actual engineering applications, and can carry out radiation calibration processing for various types of land scenes such as Martian sand, mountains, and ice surfaces. It has the characteristics of strong scene applicability, high calibration processing accuracy, and simple processing flow. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flow chart of the method proposed in the present invention.

[0068] Figure 2 It is a distributed target image acquired by the Mars ionosphere sounding SAR used in the present invention.

[0069] Figure 3 It is a distributed target image after the coherent speckle suppression processing in the present invention.

[0070] Figure 4 It is the distributed target image after homogenization processing in the present invention.

[0071] Figure 5 This is the result of reconstructing the range antenna pattern in the present invention.

[0072] Figure 6 It is the calculation result of the backscattering coefficient in the present invention.

[0073] Figure 7 It is the target backscatter coefficient in the distributed target image acquired by the Mars ionosphere sounding SAR in the present invention. DETAILED DESCRIPTION

[0074] The present invention will be described in detail below with reference to the accompanying drawings.

[0075] Since there is currently no publicly available SAR data for Mars ionosphere detection, this embodiment uses SAR images of the Qaidam Basin region acquired by the ALOS-1 satellite, which have similar environmental characteristics to the Martian surface, as the radiation calibration processing data for this embodiment. The data parameters used are shown in Table 1:

[0076] Table 1 Example parameters

[0077]

[0078]

[0079] like Figure 1As shown, a SAR radiation calibration method for Mars ionosphere detection based on distributed targets includes the following steps:

[0080] Step 1, speckle suppression processing: for the distributed target image obtained by Mars ionosphere detection SAR (such as Figure 2 As shown in the figure), Frost filtering is used to suppress the influence of coherent speckles in the distributed target image, and the distributed target image after coherent speckle suppression is obtained (as shown in the figure). Figure 3 ), specifically:

[0081] The distributed target image S with a size of X*Y obtained by the Mars ionosphere detection SAR o , where X is the number of sampling points in the azimuth direction and Y is the number of sampling points in the range direction. Frost filtering is used to realize the distributed target image S o Coherent speckle suppression, obtain the distributed target image S after coherent speckle suppression processing F Specifically, according to formulas (1) and (2), we traverse the distributed target image S o Each pixel value S ij , the distributed target image S after speckle suppression is calculated F Corresponding pixel value result S i ' j , the expression is as follows:

[0082]

[0083] Where E represents the four adjacent areas consisting of the four pixels above, below, left and right adjacent to the corresponding pixel, m p represents the weighting coefficient of the corresponding pixel, K represents the coordination factor, D represents the variance of all pixel values ​​in the four adjacent areas, d p Represents the Euclidean distance between each pixel and the corresponding pixel in the four adjacent regions, 1≤i≤X, 1≤j≤Y.

[0084] Step 2: Image homogenization: Divide the distributed target image into numerical intervals according to the pixel amplitude values ​​after the speckle suppression process, select the median of each amplitude numerical interval as the cluster center value, traverse and calculate the square difference between the amplitude of each pixel and the center value of each cluster in the distributed target image after the speckle suppression process, and use the minimum value of the square difference as the criterion to divide the numerical interval to which each pixel belongs. Construct a mask image according to the numerical interval division of the pixel, calculate the Hadamard product of the mask image and the distributed target image after the speckle suppression process, and obtain the homogenized target image (such as Figure 4 ), specifically:

[0085] Step 2.1, calculate the distributed target image S after speckle suppression processing F Cluster center value of each pixel amplitude interval:

[0086] Set the number of pixel amplitude value intervals N, and calculate the distributed target image S after speckle suppression according to formula (3): F The cluster center value M of each pixel amplitude interval n :

[0087] M n =S min +(2n-1)·(S max -S min ) / 2N (3)

[0088] Where S max Represents the distributed target image S after speckle suppression processing F The maximum pixel amplitude, S min Represents the distributed target image S after speckle suppression processing F Minimum value of pixel amplitude, n=1,2,3,…,N;

[0089] Step 2.2, calculate the distributed target image S after speckle suppression processing F The square of the difference between each pixel amplitude and the center value of each cluster:

[0090] According to formula (4), the pixel amplitude value S at coordinate (i, j) is calculated i ' j and the center value M of each cluster n The square of the difference Q n :

[0091] Q n =∑(S i ' j -M n ) 2 (4)

[0092] Step 2.3: Determine the numerical range of each pixel:

[0093] According to the difference square result Q calculated in step 2.2 n , select the minimum value Q t , where 1≤t≤N, based on which the pixel's numerical interval is determined to be interval t;

[0094] The distributed target image S after traversal speckle suppression processing F Repeat steps 2.1 to 2.3 for all pixels in the image, and count the numerical intervals to which all pixels belong;

[0095] Step 2.4, construct the mask image:

[0096] Construct the distributed target image S after speckle suppression processing F Mask image M of the same sizea Matrix, according to the pixel value interval division result obtained in step 2.3, the mask image M a The matrix is ​​assigned, and the assignment rules are as follows:

[0097] The coordinate positions of all pixels in the numerical interval t=N are in the mask image M. a The pixel amplitude in the matrix is ​​assigned a value of 0;

[0098] Mask image M a The pixel amplitudes of all other coordinate positions in the matrix are assigned a value of 1;

[0099] Step 2.5, obtain the homogenized target image:

[0100] According to formula (5), the distributed target image S after speckle suppression is calculated F With the mask image M a The Hadamard product of the matrix is ​​used to obtain the homogenized target image matrix S H , the expression is as follows:

[0101] S H =(S F ⊙M a ) X*Y (5)

[0102] Where ⊙ represents the matrix Hadamard product operation, X is the number of sampling points in the azimuth direction, and Y is the number of sampling points in the range direction.

[0103] Step 3, antenna pattern reconstruction: Calculate the average power of each sampling point in the range direction along the azimuth direction of the homogenized target image, calculate the antenna pattern results of each sampling point in the range direction based on the average power, use the least squares fitting method to process the results of each sampling point in the range direction, and obtain the reconstruction results of the antenna pattern in the range direction (such as Figure 5 ), specifically:

[0104] Step 3.1, calculate the average power of each sampling point in the range:

[0105] According to formula (6), the average power of each sampling point in the distance direction is calculated along the azimuth direction of the homogenized target image.

[0106]

[0107] Where, Represents the homogenized target image matrix S H In the element, X is the number of sampling points in the azimuth direction, and Y is the number of sampling points in the distance direction;

[0108] Step 3.2, calculate the antenna pattern:

[0109] Average power at each sampling point according to distance The antenna pattern along the distance to each sampling point is calculated by equations (7) and (8) j (1≤j≤Y):

[0110]

[0111] G j =G j-temp / max(G1,G2,...,G Y ) (8)

[0112] Where R j Indicates the slant distance corresponding to each distance sampling point, G j-temp Indicates the intermediate result of antenna pattern calculation, max(·) indicates the maximum value operation;

[0113] Step 3.3, obtain the range antenna pattern reconstruction result:

[0114] According to the antenna pattern along the distance to each sampling point result G j , the least squares fitting method is used to obtain the range antenna pattern reconstruction result G final .

[0115] Step 4, backscatter coefficient calculation: According to the range antenna pattern reconstruction result and radar system parameters, the backscatter coefficient is calculated (such as Figure 6 As shown), complete the radiation calibration process, specifically:

[0116] Reconstruct the antenna pattern G according to the distance obtained in step 3 final The backscatter coefficient σ is calculated by equation (9) with the radar system parameters:

[0117]

[0118] Where S o is the original SAR image of size X*Y, where X is the number of sampling points in azimuth, Y is the number of sampling points in range, L represents the radar signal transmission attenuation, R represents the slant range, λ represents the radar signal wavelength, and P t represents the radar system transmit power, ρ a represents the azimuth resolution, ρ r Indicates the range resolution, k a represents the azimuth gain, k r Indicates the range gain.

[0119] A Mars ionosphere detection SAR radiation calibration system based on distributed targets, comprising:

[0120] Speckle suppression processing module: For the distributed target image acquired by the Mars ionosphere sounding SAR, Frost filtering is used to suppress the influence of coherent speckle in the distributed target image, and the distributed target image after the coherent speckle suppression processing is obtained, which is used to implement step 1 of the Mars ionosphere sounding SAR radiometric calibration method based on distributed targets;

[0121] Image homogenization processing module: divide the distributed target image after speckle suppression into numerical intervals according to the pixel amplitude values, select the median of each amplitude numerical interval as the cluster center value, traverse and calculate the square difference between the amplitude of each pixel and the center value of each cluster in the distributed target image after speckle suppression, and use the minimum value of the square difference as the criterion to divide the numerical interval to which each pixel belongs. Construct a mask image according to the numerical interval division of the pixel, calculate the Hadamard product of the mask image and the distributed target image after speckle suppression, and obtain a homogenized target image, which is used to implement step 2 of the Mars ionosphere sounding SAR radiation calibration method based on distributed targets;

[0122] Antenna pattern reconstruction module: Calculate the average power of each sampling point in the range direction along the azimuth direction of the homogenized target image, calculate the antenna pattern results for each sampling point along the range direction based on the average power, use the least squares fitting method to process the antenna pattern results for each sampling point along the range direction, and obtain the range antenna pattern reconstruction results for implementing step 3 of the Mars ionosphere detection SAR radiation calibration method based on distributed targets;

[0123] Backscatter coefficient calculation module: Based on the range antenna pattern reconstruction results and radar system parameters, the backscatter coefficient is calculated and the radiation calibration processing is completed to implement step 4 of the Mars ionosphere detection SAR radiation calibration method based on distributed targets.

[0124] A SAR radiation calibration device for Mars ionosphere detection based on distributed targets, comprising:

[0125] Memory: used for storing a computer program for implementing a SAR radiation calibration method for Mars ionosphere detection based on distributed targets;

[0126] Processor: used to implement the Mars ionosphere detection SAR radiation calibration method based on distributed targets when executing the computer program.

[0127] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a SAR radiation calibration method for Mars ionosphere detection based on distributed targets.

[0128] A computer program product includes a computer program. When the computer program is executed by a processor, the computer program implements a Mars ionosphere detection SAR radiation calibration method based on distributed targets.

[0129] By comparing the target backscatter coefficient in the original SAR image (such as Figure 7 ) and the backscatter coefficient calculation results obtained by the method of the present invention (as shown in Figure 6 As shown in the figure, it can be seen that after processing using the method of the present invention, the target backscatter coefficient is essentially consistent with the true value. Further calculations show that the relative error between the backscatter coefficient obtained by the method of the present invention and the true value is 9.01%, and the relative root mean square error is 10.92%, demonstrating that the method proposed by the present invention can accurately process the target backscatter coefficient and achieve precise radiometric calibration.

Claims

1. A SAR radiation calibration method for Mars ionosphere detection based on distributed targets, characterized in that: The following steps are involved: Step 1, speckle suppression processing: For the distributed target image acquired by the Mars ionosphere sounding SAR, Frost filtering is used to suppress the speckle effect of the distributed target image and obtain the distributed target image after speckle suppression processing; Step 2: Image homogenization: Divide the distributed target image after speckle reduction into numerical intervals based on the pixel amplitude values. Select the median of each amplitude interval as the cluster center value. Calculate the squared difference between the amplitude of each pixel and the center value of each cluster in the distributed target image after speckle reduction. Use the minimum squared difference as the criterion for dividing the numerical intervals to which each pixel belongs. Construct a mask image based on the pixel numerical interval divisions. Calculate the Hadamard product between the mask image and the distributed target image after speckle reduction to obtain a homogenized target image. Step 3, antenna pattern reconstruction: Calculate the average power of each sampling point in the range direction along the azimuth direction of the homogenized target image, calculate the antenna pattern results at each sampling point along the range direction based on the average power, use the least squares fitting method to process the antenna pattern results at each sampling point along the range direction, and obtain the range antenna pattern reconstruction result; Step 4: Backscatter coefficient calculation: Based on the range antenna pattern reconstruction result and radar system parameters, the backscatter coefficient is calculated to complete the radiation calibration process.

2. The SAR radiation calibration method for Mars ionosphere detection based on distributed targets according to claim 1 is characterized in that: The specific process of the speckle reduction process in step 1 is as follows: The distributed target image S with a size of X*Y obtained by the Mars ionosphere detection SAR o , where X is the number of sampling points in the azimuth direction and Y is the number of sampling points in the range direction. Frost filtering is used to realize the distributed target image S o Coherent speckle suppression, obtain the distributed target image S after coherent speckle suppression processing F Specifically, according to formulas (1) and (2), we traverse the distributed target image S o Each pixel value S ij , the distributed target image S after speckle suppression is calculated F Corresponding pixel value result S′ ij , the expression is as follows: Where E represents the four adjacent areas consisting of the four pixels above, below, left and right adjacent to the corresponding pixel, m p represents the weighting coefficient of the corresponding pixel, K represents the coordination factor, D represents the variance of all pixel values ​​in the four adjacent areas, d p Represents the Euclidean distance between each pixel and the corresponding pixel in the four adjacent regions, 1≤i≤X, 1≤j≤Y.

3. The SAR radiation calibration method for Mars ionosphere detection based on distributed targets according to claim 1 is characterized in that: The specific process of image homogenization in step 2 is as follows: Step 2.1, calculate the distributed target image S after speckle suppression processing F Cluster center value of each pixel amplitude interval: Set the number of pixel amplitude value intervals N, and calculate the distributed target image S after speckle suppression according to formula (3): F The cluster center value M of each pixel amplitude interval n : M n =S min +(2n-1)·(S max -S min ) / 2N (3) Where S max Represents the distributed target image S after speckle suppression processing F The maximum pixel amplitude, S min Represents the distributed target image S after speckle suppression processing F Minimum value of pixel amplitude, n=1,2,3,…,N; Step 2.2, calculate the distributed target image S after speckle suppression processing F The square of the difference between each pixel amplitude and the center value of each cluster: According to formula (4), the pixel amplitude value S′ at coordinate (i, j) is calculated ij and the center value M of each cluster n The square of the difference Q n : Q n =∑(S′ ij -M n ) 2 (4) Step 2.3: Determine the numerical range of each pixel: According to the difference square result Q calculated in step 2.2 n , select the minimum value Q t , where 1≤t≤N, based on which the pixel's numerical interval is determined to be interval t; The distributed target image S after traversal speckle suppression processing F Repeat steps 2.1 to 2.3 for all pixels in the image, and count the numerical intervals to which all pixels belong; Step 2.4, construct the mask image: Construct the distributed target image S after speckle suppression processing F Mask image M of the same size a Matrix, according to the pixel value interval division result obtained in step 2.3, the mask image M a The matrix is ​​assigned, and the assignment rules are as follows: The coordinate positions of all pixels in the numerical interval t=N are in the mask image M. a The pixel amplitude in the matrix is ​​assigned a value of 0; Mask image M a The pixel amplitudes of all other coordinate positions in the matrix are assigned a value of 1; Step 2.5, obtain the homogenized target image: According to formula (5), the distributed target image S after speckle suppression is calculated F With the mask image M a The Hadamard product of the matrix is ​​used to obtain the homogenized target image matrix S H , the expression is as follows: S H =(S F ⊙M a ) X*Y (5) Where ⊙ represents the matrix Hadamard product operation, X is the number of sampling points in the azimuth direction, and Y is the number of sampling points in the range direction.

4. The SAR radiation calibration method for Mars ionosphere detection based on distributed targets according to claim 1 is characterized in that: The specific process of antenna pattern reconstruction in step 3 is as follows: Step 3.1, calculate the average power of each sampling point in the range: According to formula (6), the average power of each sampling point in the distance direction is calculated along the azimuth direction of the homogenized target image. Where, Represents the homogenized target image matrix S H In the element, X is the number of sampling points in the azimuth direction, and Y is the number of sampling points in the distance direction; Step 3.2, calculate the antenna pattern: Average power at each sampling point according to distance The antenna pattern along the distance to each sampling point is calculated by equations (7) and (8) j (1≤j≤Y): G j =G j-temp / max(G1,G2,...,G Y ) (8) Where R j Indicates the slant distance corresponding to each distance sampling point, G j-temp Indicates the intermediate result of antenna pattern calculation, max(·) indicates the maximum value operation; Step 3.3, obtain the range antenna pattern reconstruction result: According to the antenna pattern along the distance to each sampling point result G j , the least squares fitting method is used to obtain the range antenna pattern reconstruction result G final .

5. The SAR radiation calibration method for Mars ionosphere detection based on distributed targets according to claim 1, characterized in that: The specific process of backscatter coefficient calculation in step 4 is as follows: Reconstruct the antenna pattern G according to the distance obtained in step 3 final The backscatter coefficient σ is calculated by equation (9) with the radar system parameters: Where S o is the original SAR image of size X*Y, where X is the number of sampling points in azimuth, Y is the number of sampling points in range, L represents the radar signal transmission attenuation, R represents the slant range, λ represents the radar signal wavelength, and P t represents the radar system transmit power, ρ a represents the azimuth resolution, ρ r Indicates the range resolution, k a represents the azimuth gain, k r Indicates the range gain.

6. A Mars ionosphere detection SAR radiation calibration system based on distributed targets based on the method according to any one of claims 1 to 5, characterized in that: include: Coherent speckle suppression processing module: For the distributed target images obtained by the Mars ionosphere sounding SAR, Frost filtering is used to suppress the coherent speckle effect of the distributed target images and obtain the distributed target images after coherent speckle suppression processing; Image homogenization processing module: divide the numerical intervals according to the pixel amplitude values ​​of each distributed target image after the coherent speckle suppression process, select the median of each amplitude numerical interval as the cluster center value, traverse and calculate the square difference between the amplitude of each pixel and the center value of each cluster in the distributed target image after the coherent speckle suppression process, and use the minimum value of the square difference as the criterion to divide the numerical interval to which each pixel belongs. Construct a mask image based on the numerical interval division of the pixel, and calculate the Hadamard product between the mask image and the distributed target image after the coherent speckle suppression process to obtain a homogenized target image; Antenna pattern reconstruction module: Calculate the average power of each sampling point in the range direction along the azimuth direction of the homogenized target image, calculate the antenna pattern results at each sampling point along the range direction based on the average power, use the least squares fitting method to process the antenna pattern results at each sampling point along the range direction, and obtain the range antenna pattern reconstruction results; Backscatter coefficient calculation module: Based on the range antenna pattern reconstruction results and radar system parameters, the backscatter coefficient is calculated to complete the radiation calibration process.

7. A SAR radiation calibration device for Mars ionosphere detection based on distributed targets, characterized in that: include: Memory: used to store a computer program for implementing the SAR radiation calibration method for Mars ionosphere detection based on distributed targets as described in any one of claims 1 to 5; Processor: configured to implement the Mars ionosphere detection SAR radiation calibration method based on distributed targets as described in any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the Mars ionosphere detection SAR radiation calibration method based on distributed targets according to any one of claims 1 to 5 are implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the SAR radiation calibration method for Mars ionosphere detection based on distributed targets as described in any one of claims 1 to 5 is implemented.

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