Mass SAR data imaging software design method based on fast BP algorithm

Abstract

The invention relates to a massive SAR (Synthetic Aperture Radar) data imaging software design method based on a rapid BP (Back Propagation) algorithm, which aims at an existing rapid BP imaging algorithm, and provides a method for dynamically correcting J operators by utilizing an available memory of an operation server, carrying out sub-set processing on sub-scenes and temporarily storing intermediate operation results by utilizing a disk by accurately estimating the data volume in an operation process. The problem of memory limitation of SAR full-scene full-resolution imaging of ultra-large data on an operation server with a lower memory is solved, and the hardware memory cost of the operation server is greatly reduced.

Description

Technical Field

[0001] The present application relates to the field of SAR imaging, and in particular, to a method for designing software for massive SAR data imaging based on a fast BP algorithm. Background Art

[0002] In response to the imaging requirements of high-orbit (geosynchronous orbit), the SAR payload obtains a longer target azimuth coherent accumulation time through azimuth antenna beam staring scanning to achieve better resolution in the target azimuth dimension. The resolution in the range dimension is determined by the system bandwidth. The larger the bandwidth, the higher the resolution, and the corresponding imaging calculation amount will also increase. In order to achieve better azimuth resolution, the synthetic aperture time of a single imaging in the high-orbit SAR strip mode can reach 1800s to 3600s, which correspondingly brings about the problem of a large amount of imaging echo data within a single synthetic aperture time. The high-orbit SAR satellite strip mode can achieve 300km×500km range imaging in a single startup, which has the characteristics of ultra-wide width. Therefore, full-scene full-resolution image data also requires a huge memory space. In order to complete stable imaging of the full scene on the limited computing server memory, the imaging process must solve the problems of separately processing the echo and image scene in blocks, clarifying the memory requirements in each SAR imaging step, and reasonably designing the software process. In the prior art, there is a problem of memory limitation for SAR full-scene full-resolution imaging of super large data on computing servers with lower memory. Summary of the invention

[0003] In order to overcome at least one deficiency in the prior art, the present application provides a method for designing massive SAR data imaging software based on a fast BP algorithm.

[0004] In a first aspect, a method for designing software for massive SAR data imaging based on a fast BP algorithm is provided, comprising:

[0005] Step 1: Divide the full scene single-view single-precision complex image into C r ×C a sub-scene images, C r Indicates the number of sub-scene images in the range direction, C a Indicates the number of sub-scene images in the azimuth direction, and divides all sub-scene images into J subsets, where the initial value of J is 1, including:

[0006] Solution 1: Divide all sub-scene images into J subsets along the azimuth or distance direction;

[0007] Solution 2: Divide all sub-scene images into J subsets;

[0008] There is an overlapping area between two adjacent sub-scene images;

[0009] Step 2, under the current J value, according to the width of the sub-scene images, calculate the memory space occupied by all sub-scene images, calculate the memory space occupied by the overlapping areas of all sub-scene images, and calculate the memory space occupied by the three-dimensional geographic grid points that fast BP imaging depends on;

[0010] Step 3: When performing SAR imaging based on the fast BP algorithm, it is necessary to divide the echo signal into sub-apertures to obtain multiple sub-aperture signals; the memory space occupied by the echo data of the sub-aperture is calculated according to the number of pulses in the azimuth direction of the sub-aperture and the number of sampling points in the range direction;

[0011] Step 4, calculate the peak memory consumption in the SAR imaging process according to the memory space occupied by all sub-scene images, the memory space occupied by the overlapping areas of all sub-scene images, the memory space occupied by the three-dimensional geographic grid points on which the fast BP imaging depends, and the memory space occupied by the echo data of the sub-aperture;

[0012] Step 5, determine whether the peak memory consumption during the SAR imaging process is greater than the available memory of the computing server. If so, increase the J value by 1 and return to step 2. If not, execute step 6.

[0013] Step 6: For solution 1, determine whether the J value is less than or equal to the number of azimuth sub-scene images C a Or the number of distance-wise sub-scene images C r , for solution 2, determine whether the J value is less than or equal to the number of sub-scene images C r ×C a If so, the memory of the computing server can realize full scene imaging and obtain the final J value. If not, the memory of the computing server is insufficient to realize full scene imaging.

[0014] In one embodiment, the full-scene single-view single-precision complex image is divided into a plurality of sub-scene images, and the division rules used include:

[0015] The two-dimensional width of each sub-scene image is consistent and greater than a first set value, the single-view 16-bit complex image corresponding to each sub-scene image is smaller than a second set value, and there is an overlapping area between two adjacent sub-scene images.

[0016] In one embodiment, the memory space occupied by all sub-scene images is expressed by the following formula:

[0017]

[0018] Among them, M fig is the memory space occupied by all sub-scene images, j is the sequence number of the subset, J is the number of subsets, N j is the number of sub-scene images in the j-th subset, n is the sequence number of the sub-scene images in the j-th subset, Wn is the actual width of the nth sub-scene image, I r is the distance pixel spacing of the image, I a is the pixel spacing of the image in azimuth.

[0019] In one embodiment, the memory space occupied by the overlapping areas of all sub-scene images is expressed by the following formula:

[0020]

[0021] Among them, M ovp is the memory space occupied by the overlapping area of ​​all sub-scene images, j is the sequence number of the subset, J is the number of subsets, N j is the number of sub-scene images in the j-th subset, n is the sequence number of the sub-scene images in the j-th subset, W n is the actual width of the nth sub-scene image, I r is the distance pixel spacing of the image, I a is the azimuth pixel spacing of the image, and α is the ratio of the overlapping area to the sub-scene image.

[0022] In one embodiment, the memory space occupied by the three-dimensional geographic grid points that the fast BP imaging relies on is expressed by the following formula:

[0023]

[0024] Among them, M tar is the memory space occupied by the three-dimensional geographic grid points that fast BP imaging relies on, M ovp is the memory space occupied by the overlapping area of ​​all sub-scene images, M fig The memory space occupied by all sub-scene images, D n It is the downsampling multiple of the geographic grid point coordinates along the azimuth direction.

[0025] In one embodiment, the memory space occupied by the echo data of the sub-aperture is expressed by the following formula:

[0026] M apt =N k ×N r ×8

[0027]

[0028] Among them, M apt is the memory space occupied by the sub-aperture echo data, N k is the number of azimuth pulses of the subaperture, N r is the number of sampling points in the echo range, N a is the number of pulses in the full aperture time in azimuth, T ais the full aperture time, T k is the single subaperture time, To round down, To round up.

[0029] In one embodiment, calculating the peak memory consumption during SAR imaging includes:

[0030] For scheme 1, the SAR imaging process includes: for each subset, sub-aperture imaging, sub-aperture upsampling fusion and first-level stitching of the sub-scene images in the subset are performed in turn to obtain the stitched image corresponding to the subset and write it to the disk; after each subset completes the image stitching, the stitched images corresponding to the subset are read from the disk one by one according to the order of the subsets, and the second-level stitching is performed in turn to achieve full-scene imaging;

[0031] Calculate the memory consumed by subaperture imaging of each subset:

[0032]

[0033] Among them, (M img ) j is the memory consumed by the jth subset subaperture imaging, M apt is the memory space occupied by the sub-aperture echo data, N j is the number of sub-scene images in the j-th subset, n is the sequence number of the sub-scene images in the j-th subset, W n is the actual width of the nth sub-scene image, I r is the distance pixel spacing of the image, I a is the azimuth pixel spacing of the image, M tar is the memory space occupied by the three-dimensional geographic grid points that fast BP imaging relies on, M aux Memory space occupied by auxiliary data;

[0034] Memory required to calculate the stitching of the entire scene:

[0035]

[0036] Among them, M spl The memory required for stitching the entire scene, M fig is the memory space occupied by all sub-scene images, M ovp The memory space occupied by the overlapping area of ​​all sub-scene images;

[0037] The peak memory consumption during SAR imaging is:

[0038] M max =max(max((M img ) j ),Mspl )

[0039] Among them, M max is the peak memory consumption.

[0040] In one embodiment, calculating the peak memory consumption during SAR imaging includes:

[0041] For scheme 2, the SAR imaging process includes: dividing all sub-scene images into J subsets, and for each subset, performing sub-aperture imaging and sub-aperture upsampling fusion on the sub-scene images in the subset in turn to obtain the up-sampled fused image, and writing it to the disk; after the upsampling fusion of each sub-scene image is completed, the up-sampled fused images are read from the disk one by one according to the order of the sub-scene images, and spliced ​​one by one to achieve full-scene imaging;

[0042] Calculate the memory consumed by subaperture imaging of each subset:

[0043]

[0044] Among them, (M img ) j is the memory consumed by the jth subset subaperture imaging, M apt is the memory space occupied by the sub-aperture echo data, N j is the number of sub-scene images in the j-th subset, n is the sequence number of the sub-scene images in the j-th subset, W n is the actual width of the nth sub-scene image, I r is the distance pixel spacing of the image, I a is the azimuth pixel spacing of the image, M tar is the memory space occupied by the three-dimensional geographic grid points that fast BP imaging relies on, M aux Memory space occupied by auxiliary data;

[0045] Memory required to calculate the stitching of the entire scene:

[0046]

[0047] Among them, M spl The memory required for stitching the entire scene, M fig is the memory space occupied by all sub-scene images, M ovp The memory space occupied by the overlapping area of ​​all sub-scene images;

[0048] The peak memory consumption during SAR imaging is:

[0049] M max =max(max((M img ) j),M spl )

[0050] Among them, M max is the peak memory consumption.

[0051] In a second aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for designing software for massive SAR data imaging based on a fast BP algorithm is implemented.

[0052] In a third aspect, a computer program product is provided, including a computer program / instruction. When the computer program / instruction is executed by a processor, the method for designing software for massive SAR data imaging based on a fast BP algorithm is implemented.

[0053] Compared with the prior art, the present application has the following beneficial effects: This application targets the existing fast BP imaging algorithm, and by accurately estimating the amount of data in the operation process, proposes to utilize the available memory of the operation server, dynamically correct the J operator, perform molecular set processing on the sub-scenes, and use the disk to temporarily store the intermediate operation results, thereby solving the memory limitation problem of SAR full-scene full-resolution imaging of very large data on a operation server with lower memory, and greatly reducing the hardware memory cost of the operation server. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present application may be better understood by referring to the following description given in conjunction with the accompanying drawings, which together with the following detailed description are included in this specification and form a part of this specification. In the drawings:

[0055] Figure 1 The flowchart of the software design method for massive SAR data imaging based on fast BP algorithm is shown;

[0056] Figure 2 A schematic diagram of one of the unequal scene division methods in scheme 2 is shown;

[0057] Figure 3 A schematic diagram of sub-scene division is shown. DETAILED DESCRIPTION

[0058] The exemplary embodiments of the present application will be described below in conjunction with the accompanying drawings. For the sake of clarity and conciseness, not all features of the actual embodiments are described in the specification. However, it should be understood that many implementation-specific decisions can be made in the process of developing any such actual embodiments in order to achieve the specific goals of the developer, and these decisions may vary from embodiment to embodiment.

[0059] It is also necessary to explain here that, in order to avoid obscuring the present application due to unnecessary details, only the device structure closely related to the scheme according to the present application is shown in the drawings, while other details that are not closely related to the present application are omitted.

[0060] It should be understood that the present application is not limited to the described implementation forms due to the following description with reference to the accompanying drawings. In this article, where feasible, the embodiments can be combined with each other, features between different embodiments can be replaced or borrowed, and one or more features can be omitted in one embodiment.

[0061] The present application embodiment provides a method for designing software for mass SAR data imaging based on a fast BP (BackProjection) algorithm. Figure 1 A flowchart of a method for designing software for massive SAR data imaging based on a fast BP algorithm is shown in FIG. Figure 1 , methods include:

[0062] Step 1: Divide the full scene single-view single-precision complex image into C r ×C a sub-scene images, C r Indicates the number of sub-scene images in the range direction, C a Indicates the number of sub-scene images in the azimuth direction, C r and C a All of them can be equal to 1, and all sub-scene images are divided into J subsets, the initial value of J is 1, including:

[0063] Solution 1: Divide all sub-scene images into J subsets along the azimuth or distance direction;

[0064] Solution 2: Divide all sub-scene images into J subsets; here, an average or non-average distribution can be performed. For example, when the total number of sub-scene images is 9 and J is equal to 2, it can be divided into 4+5. Figure 2 A schematic diagram of one of the unequal scene division methods in Solution 2 is shown.

[0065] There is an overlapping area between two adjacent sub-scene images.

[0066] Among them, single view: means that the image has not been processed by multiple views and retains the original resolution. Single precision: means that the image data is stored in single precision floating point (32 bits). Complex image: means that the image data contains real and imaginary parts, which can express the amplitude and phase information of the radar signal.

[0067] Specifically, the division rules adopted include:

[0068] (1) The two-dimensional width of each sub-scene image is consistent and greater than the first set value W min , here, Wmin is any value greater than 0 that is smaller than the lower one-dimensional width of the full scene, determined according to actual needs; (2) the single-view 16-bit complex image corresponding to each sub-scene image is smaller than the second set value, where the second set value is any value greater than 0 that is smaller than the data size of the single-view 16-bit complex image of the full scene, determined according to actual needs; (3) there is an overlapping area between two adjacent sub-scene images, where the overlapping area is 10% as an example, Figure 3 A schematic diagram of sub-scene division is shown.

[0069] Using C r Indicates the number of sub-scenes divided by distance (C r is an integer and C r ≥1), use C a Indicates the number of sub-scene images divided in the azimuth direction (C a is an integer and C a ≥1), the total number of sub-scene images is C r ×C a All sub-scene images are divided into J subsets, and the number of sub-scenes contained in the jth subset is N j Indicates that j=1,2,…J.

[0070] Step 2: Under the current J value, according to the width of the sub-scene images, calculate the memory space occupied by all sub-scene images, calculate the memory space occupied by the overlapping areas of all sub-scene images, and calculate the memory space occupied by the three-dimensional geographic grid points on which fast BP imaging depends.

[0071] According to the above width and image byte size as constraints, when the single-view 16-bit complex image of a single sub-scene is less than 8GB, its corresponding single-view single-precision complex image data must be between 14GB and 16GB. The actual width of the sub-scene image is determined according to the following formula:

[0072]

[0073] Where W is the width of the smaller dimension of the whole scene. If the width in azimuth is smaller, then W is the width in azimuth of the whole scene; if the width in distance is smaller, then W is the width in distance of the whole scene; W min is the first set value; W n is the width of the nth sub-scene image, in meters, I r is the distance pixel spacing of the image, in meters, I a is the pixel spacing in the azimuth direction of the image, in meters. The scene width W calculated by the above inequality group is n It should be as large as possible under limited conditions.

[0074] Specifically, the memory space occupied by all sub-scene images is expressed by the following formula:

[0075]

[0076] Among them, M fig is the memory space occupied by all sub-scene images, j is the sequence number of the subset, J is the number of subsets, N j is the number of sub-scene images in the j-th subset, n is the sequence number of the sub-scene images in the j-th subset, W n is the actual width of the nth sub-scene image, I r is the distance pixel spacing of the image, I a is the pixel spacing of the image in azimuth.

[0077] Specifically, the memory space occupied by the overlapping areas of all sub-scene images is expressed by the following formula:

[0078]

[0079] Among them, M ovp is the memory space occupied by the overlapping area of ​​all sub-scene images, j is the sequence number of the subset, J is the number of subsets, N j is the number of sub-scene images in the j-th subset, n is the sequence number of the sub-scene images in the j-th subset, W n is the actual width of the nth sub-scene image, I r is the distance pixel spacing of the image, I a is the azimuth pixel spacing of the image, and β is the ratio of the overlapping area to the sub-scene image, which may be 10%, for example.

[0080] Specifically, the memory space occupied by the three-dimensional geographic grid points that fast BP imaging relies on is expressed by the following formula:

[0081]

[0082] Among them, M tar is the memory space occupied by the three-dimensional geographic grid points that fast BP imaging relies on, M ovp is the memory space occupied by the overlapping area of ​​all sub-scene images, M fig The memory space occupied by all sub-scene images, D n It is the downsampling multiple of the geographic grid point coordinates along the azimuth direction.

[0083] Step 3, when performing SAR imaging based on the fast BP algorithm, it is necessary to divide the echo signal into sub-apertures to obtain multiple sub-aperture signals; according to the number of azimuth pulses of the sub-aperture combined with the number of sampling points in the range, calculate the memory space occupied by the echo data of the sub-aperture.

[0084] Specifically, the memory space occupied by the echo data of the sub-aperture is expressed by the following formula:

[0085] M apt =N k ×N r ×8

[0086]

[0087] Among them, M apt is the memory space occupied by the sub-aperture echo data, N k is the number of azimuth pulses of the subaperture, N r is the number of sampling points in the echo range, N a is the number of pulses in the full aperture time in azimuth, T a is the full aperture time, T k is the single subaperture time, To round down, To round up.

[0088] Step 4: Calculate the peak memory consumption during the SAR imaging process based on the memory space occupied by all sub-scene images, the memory space occupied by the overlapping areas of all sub-scene images, the memory space occupied by the three-dimensional geographic grid points on which fast BP imaging depends, and the memory space occupied by the echo data of the sub-aperture.

[0089] There are two schemes that can be used to calculate the peak memory consumption during SAR imaging.

[0090] In scheme 1, the SAR imaging process includes: for each subset, sub-aperture imaging, sub-aperture upsampling fusion and primary stitching of sub-scene images in the subset are performed to obtain the stitched image corresponding to the subset and write it to the disk; after each subset completes the image stitching, the stitched images corresponding to the subset are read from the disk one by one according to the order of the subsets, and secondary stitching is performed in sequence to achieve full scene imaging;

[0091] Calculate the memory consumed by subaperture imaging of each subset:

[0092]

[0093] Among them, (M img ) j is the memory consumed by the jth subset subaperture imaging, M apt is the memory space occupied by the sub-aperture echo data, N j is the number of sub-scene images in the j-th subset, n is the sequence number of the sub-scene images in the j-th subset, W n is the actual width of the nth sub-scene image, I r is the distance pixel spacing of the image, Ia is the azimuth pixel spacing of the image, M tar is the memory space occupied by the three-dimensional geographic grid points that fast BP imaging relies on, M aux Memory space occupied by auxiliary data;

[0094] Calculate the memory consumed by sub-aperture upsampling fusion:

[0095]

[0096] It can be seen that (M img ) j >(M up ) j .

[0097] Memory required to calculate the stitching of the entire scene:

[0098]

[0099] Among them, M spl The memory required for stitching the entire scene, M fig is the memory space occupied by all sub-scene images, M ovp The memory space occupied by the overlapping area of ​​all sub-scene images;

[0100] The peak memory consumption during SAR imaging is:

[0101] M max =max(max((M img ) j ),M spl )

[0102] Among them, M max is the peak memory consumption.

[0103] In the second scheme, the SAR imaging process includes: for each subset, sub-aperture imaging and sub-aperture upsampling and fusion are performed on the sub-scene images in the subset to obtain the up-sampled and fused images, and write them to the disk; after the upsampling and fusion of each sub-scene image is completed, the up-sampled and fused images are read from the disk one by one according to the order of the sub-scene images, and they are stitched one by one to achieve full-scene imaging;

[0104] Calculate the memory consumed by subaperture imaging of each subset:

[0105]

[0106] Among them, (M img ) j is the memory consumed by the jth subset subaperture imaging, M aptis the memory space occupied by the sub-aperture echo data, N j is the number of sub-scene images in the j-th subset, n is the sequence number of the sub-scene images in the j-th subset, W n is the actual width of the nth sub-scene image, I r is the distance pixel spacing of the image, I a is the azimuth pixel spacing of the image, M tar is the memory space occupied by the three-dimensional geographic grid points that fast BP imaging relies on, M aux Memory space occupied by auxiliary data;

[0107] Memory required to calculate the stitching of the entire scene:

[0108]

[0109] Among them, M spl The memory required for stitching the entire scene, M fig is the memory space occupied by all sub-scene images, M ovp The memory space occupied by the overlapping area of ​​all sub-scene images;

[0110] The peak memory consumption during SAR imaging is:

[0111] M max =max(max((M img ) j ),M spl )

[0112] Among them, M max is the peak memory consumption.

[0113] Comparing the peak memory consumption in Scheme 2 and Scheme 1, it can be seen that the memory consumption of Scheme 2 in this step is less than that of Scheme 1 in this step, that is, Scheme 2 has a better peak memory when splicing sub-scenes, but in actual applications, the software process designed in Scheme 1 is more flexible.

[0114] Step 5, determine whether the peak memory consumption during the SAR imaging process is greater than the available memory M of the computing server. If so, increase the J value by 1 and return to step 2. If not, execute step 6.

[0115] Step 6: For solution 1, determine whether the J value is less than or equal to the number of azimuth sub-scene images C a Or the number of distance-wise sub-scene images C r , for solution 2, determine whether the J value is less than or equal to the number of sub-scene images C r ×C aIf so, the memory of the computing server can realize full scene imaging and obtain the final J value. If not, the memory of the computing server is insufficient to realize full scene imaging.

[0116] In the imaging software design method of this embodiment, in the sub-scene full-resolution imaging, all sub-aperture echo signals are required for each sub-scene imaging, so the J value should be as small as possible to reduce the frequency of repeated analysis of the echo signal. That is, after each sub-aperture echo signal is analyzed, as many sub-scene sub-aperture imaging as possible should be completed. However, the more sub-scenes there are, the more memory is consumed, so the J value needs to be used to adjust the number of sub-scene imaging completed by a single analysis of the sub-aperture echo.

[0117] In summary, compared with the prior art, the present application has the following technical effects: This application targets the existing fast BP imaging algorithm, and by accurately estimating the amount of data in the calculation process, proposes to use the available memory of the computing server, dynamically correct the J operator, perform molecular set processing on the sub-scenes, and use the disk to temporarily store intermediate calculation results, thereby solving the memory limitation problem of SAR full-scene full-resolution imaging of very large data on a computing server with lower memory, and greatly reducing the hardware memory cost of the computing server.

[0118] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for designing software for massive SAR data imaging based on a fast BP algorithm.

[0119] The embodiment of the present application provides a computer program product, including a computer program / instruction. When the computer program / instruction is executed by a processor, the method for designing software for massive SAR data imaging based on a fast BP algorithm is implemented.

[0120] The above are only various implementations of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for designing massive SAR data imaging software based on fast BP algorithm, characterized in that: include: Step 1: Divide the full scene single-view single-precision complex image into C r ×C a sub-scene images, C r Indicates the number of sub-scene images in the range direction, C a Indicates the number of sub-scene images in the azimuth direction, and divides all sub-scene images into J subsets. The initial value of J is 1, including: Solution 1: Divide all sub-scene images into J subsets along the azimuth or distance direction; Solution 2: Divide all sub-scene images into J subsets; There is an overlapping area between two adjacent sub-scene images; Step 2, under the current J value, according to the width of the sub-scene images, calculate the memory space occupied by all sub-scene images, calculate the memory space occupied by the overlapping areas of all sub-scene images, and calculate the memory space occupied by the three-dimensional geographic grid points that fast BP imaging depends on; Step 3: When performing SAR imaging based on the fast BP algorithm, it is necessary to divide the echo signal into sub-apertures to obtain multiple sub-aperture signals; the memory space occupied by the echo data of the sub-aperture is calculated according to the number of pulses in the azimuth direction of the sub-aperture and the number of sampling points in the range direction; Step 4, calculating the peak memory consumption in the SAR imaging process according to the memory space occupied by all the sub-scene images, the memory space occupied by the overlapping areas of all the sub-scene images, the memory space occupied by the three-dimensional geographic grid points on which the fast BP imaging depends, and the memory space occupied by the echo data of the sub-aperture; Step 5, determine whether the peak memory consumption during the SAR imaging process is greater than the available memory of the computing server. If so, increase the J value by 1 and return to step 2. If not, execute step 6. Step 6: For solution 1, determine whether the J value is less than or equal to the number of azimuth sub-scene images C a Or the number of distance-wise sub-scene images C r , for solution 2, determine whether the J value is less than or equal to the number of sub-scene images C r ×C a If so, the memory of the computing server can realize full scene imaging and obtain the final J value. If not, the memory of the computing server is insufficient to realize full scene imaging.

2. The method according to claim 1, characterized in that in, The full-scene single-view single-precision complex image is divided into multiple sub-scene images. The division rules used include: The two-dimensional width of each sub-scene image is consistent and greater than a first set value, the single-view 16-bit complex image corresponding to each sub-scene image is smaller than a second set value, and there is an overlapping area between two adjacent sub-scene images.

3. The method according to claim 1, characterized in that The memory space occupied by all sub-scene images is expressed by the following formula: Among them, M fig is the memory space occupied by all sub-scene images, j is the sequence number of the subset, J is the number of subsets, N j is the number of sub-scene images in the j-th subset, n is the sequence number of the sub-scene images in the j-th subset, W n is the actual width of the nth sub-scene image, I r is the distance pixel spacing of the image, I a is the pixel spacing of the image in azimuth.

4. The method according to claim 1, characterized in that The memory space occupied by the overlapping areas of all sub-scene images is expressed by the following formula: Among them, M ovp is the memory space occupied by the overlapping area of ​​all sub-scene images, j is the sequence number of the subset, J is the number of subsets, N j is the number of sub-scene images in the j-th subset, n is the sequence number of the sub-scene images in the j-th subset, W n is the actual width of the nth sub-scene image, I r is the distance pixel spacing of the image, I a is the azimuth pixel spacing of the image, and α is the ratio of the overlapping area to the sub-scene image.

5. The method according to claim 1, characterized in that The memory space occupied by the three-dimensional geographic grid points on which the fast BP imaging depends is expressed by the following formula: Among them, M tar is the memory space occupied by the three-dimensional geographic grid points that fast BP imaging relies on, M ovp is the memory space occupied by the overlapping area of ​​all sub-scene images, M fig The memory space occupied by all sub-scene images, D n It is the downsampling multiple of the geographic grid point coordinates along the azimuth direction.

6. The method according to claim 1, characterized in that The memory space occupied by the echo data of the sub-aperture is expressed by the following formula: M apt =N k ×N r ×8 Among them, M apt is the memory space occupied by the sub-aperture echo data, N k is the number of azimuth pulses of the subaperture, N r N is the number of sampling points in the echo range direction. a is the number of pulses in the full aperture time in azimuth, T a is the full aperture time, T k is the single subaperture time, To round down, To round up.

7. The method according to claim 1, characterized in that The peak memory consumption during the calculation of SAR imaging includes: For scheme 1, the SAR imaging process includes: for each subset, performing sub-aperture imaging, sub-aperture upsampling fusion and primary stitching of sub-scene images in the subset in turn, obtaining a stitched image corresponding to the subset, and writing it to a disk; after each subset completes image stitching, the stitched images corresponding to the subset are read from the disk one by one according to the order of the subsets, and secondary stitching is performed in turn to achieve full scene imaging; Calculate the memory consumed by each subset subaperture imaging: Among them, (M img ) j is the memory consumed by the jth subset subaperture imaging, M apt is the memory space occupied by the sub-aperture echo data, N j is the number of sub-scene images in the j-th subset, n is the sequence number of the sub-scene images in the j-th subset, W n is the actual width of the nth sub-scene image, I r is the distance pixel spacing of the image, I a is the azimuth pixel spacing of the image, M tar is the memory space occupied by the three-dimensional geographic grid points that fast BP imaging relies on, M aux Memory space occupied by auxiliary data; Memory required to calculate the stitching of the entire scene: Among them, M spl The memory required for stitching the entire scene, M fig is the memory space occupied by all sub-scene images, M ovp The memory space occupied by the overlapping area of ​​all sub-scene images; The peak memory consumption during SAR imaging is: M max =max(max((M img ) j ),M spl ) Among them, M max is the peak memory consumption.

8. The method according to claim 1, characterized in that The peak memory consumption during the calculation of SAR imaging includes: For scheme 2, the SAR imaging process includes: dividing all sub-scene images into J subsets, and for each subset, sequentially performing sub-aperture imaging and sub-aperture upsampling fusion on the sub-scene images in the subset to obtain up-sampled fused images, and writing them to the disk; after all sub-scene images have completed image upsampling fusion, the up-sampled fused images are read from the disk one by one according to the order of the sub-scene images, and spliced ​​one by one to achieve full-scene imaging; Calculate the memory consumed by each subset subaperture imaging: Among them, (M img ) j is the memory consumed by the jth subset subaperture imaging, M apt is the memory space occupied by the sub-aperture echo data, N j is the number of sub-scene images in the j-th subset, n is the sequence number of the sub-scene images in the j-th subset, W n is the actual width of the nth sub-scene image, I r is the distance pixel spacing of the image, I a is the azimuth pixel spacing of the image, M tar is the memory space occupied by the three-dimensional geographic grid points that fast BP imaging relies on, M aux Memory space occupied by auxiliary data; Memory required to calculate the stitching of the entire scene: Among them, M spl The memory required for stitching the entire scene, M fig is the memory space occupied by all sub-scene images, M ovp The memory space occupied by the overlapping area of ​​all sub-scene images; The peak memory consumption during SAR imaging is: M max =max(max((M img ) j ),M spl ) Among them, M max is the peak memory consumption.

9. 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 method for designing software for massive SAR data imaging based on a fast BP algorithm according to any one of claims 1 to 8 is implemented.

10. A computer program product, characterized in that It includes a computer program / instruction, which, when executed by a processor, implements the method for designing massive SAR data imaging software based on a fast BP algorithm as described in any one of claims 1 to 8.

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