A terrain correction geocoding method, system, device and medium

Abstract

The present invention discloses a terrain correction geocoding method, system, device, and medium. The method performs image transformation processing on the acquired multi-view amplitude data to obtain a strong scattering area mask. Based on the acquired digital surface model data, multi-view radar parameter data, and multi-view amplitude data, the method obtains the first overlapping area mask and radar domain coordinates of the ground object target in the digital surface model data in the radar domain coordinate system, the second overlapping area mask of the ground object target in the latitude and longitude coordinate system, and the geocoding coordinates and weights of each overlapping pixel point corresponding to the second overlapping area mask. The strong scattering area mask and the first overlapping area mask are matched to obtain an overlapping area. The overlapping area is corrected using a correction amount model constructed based on the geocoding coordinates and weights to obtain corrected image data. The method provided by the present application has more accurate correction, does not rely on simulated SAR imaging algorithms, nor on image matching algorithms, and has higher adaptability.

Description

Technical Field

[0001] The present invention relates to the field of image correction technology, and in particular to a terrain correction geocoding method, system, device and medium. Background Art

[0002] Synthetic Aperture Radar (SAR), an active microwave imaging sensor, boasts advantages such as all-day, all-weather coverage, wide coverage, and strong penetration. It has played a vital role in geological mapping, land resource surveys, and environmental disaster monitoring. However, due to the side-viewing geometry of SAR systems, the resulting SAR squint images are susceptible to geometric distortions such as perspective contraction and top-bottom inversion caused by the terrain of the observed scene, hindering the identification of ground objects. Furthermore, the lack of geographic location information in SAR squint images further limits their application scenarios. Therefore, before practical application, SAR images must be converted into orthophotos using a technique called Geocoded Terrain Corrected (GTC). This technology eliminates or mitigates the distortion caused by side-viewing and complex terrain, ensuring that the SAR images have a true geographic location and reflect realistic surface information.

[0003] However, SAR systems are subject to various error sources during Earth observation, such as sensor internal noise, platform orbit positioning errors, atmospheric propagation delays in echo signals, and ground elevation errors. Therefore, the image data acquired by the SAR system needs to be corrected to improve accuracy. To improve the accuracy of terrain-corrected geocoding, the following three improved terrain-corrected geocoding methods are used in existing technologies:

[0004] Method 1: Based on the known high-precision control points in the geometric calibration field, the positioning model is adjusted to obtain more accurate radar parameters;

[0005] Method 2: Using a digital surface model (DSM) to simulate a SAR squint image of the imaging scene, the simulated SAR squint image is matched with the real SAR squint image to refine the geocoding lookup table.

[0006] Method three: select the optical orthophoto of the same scene as the reference map, perform image matching between the optical orthophoto and the SAR orthophoto, and directly correct the geographic location of the SAR orthophoto to the optical reference map.

[0007] Method 1 has strict requirements on the number, distribution, and accuracy of control points. Reliable control points are difficult to obtain in some extreme terrains, limiting its applicability. Both Methods 2 and 3 rely on heterogeneous image matching algorithms, which require the ability to extract reasonably distributed and accurately aligned pairs of identical points between simulated and real SAR images. This places high demands on the matching algorithm and has limited compatibility with traditional matching algorithms, resulting in a relatively limited scope for both Methods 2 and 3.

[0008] It can be seen that designing a terrain correction geocoding method to improve universality has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0009] The present invention provides a terrain correction geocoding method, system, device and medium to solve the technical problem of designing a terrain correction geocoding method to improve universality, thereby achieving the effect of improving the universality of terrain correction geocoding.

[0010] In a first aspect, the present invention provides a terrain correction geocoding method, the method comprising: acquiring digital surface model data of a target area, radar parameter data, and image data acquired by a SAR system; performing multi-look processing on the radar parameter data to obtain multi-look radar parameter data; and performing multi-look processing on the image data to obtain multi-look amplitude data;

[0011] Performing image transformation processing on the multi-view amplitude data to obtain a strong scattering area mask;

[0012] Obtaining, based on the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data, a first overlay region mask of a ground object in the digital surface model data in a radar domain coordinate system and the radar domain coordinates of the ground object; determining, based on the digital surface model data and the radar domain coordinates, a second overlay region mask of the ground object in a latitude and longitude coordinate system, and determining the geocoded coordinates and weight of each overlay pixel point corresponding to the second overlay region mask; and matching the strong scattering region mask with the first overlay region mask to obtain an overlapping region between the first overlay region mask and the strong scattering region mask;

[0013] constructing a correction amount model for the target area according to each of the geocoded coordinates and the weight;

[0014] The correction amount model is used to correct the overlapping area to obtain corrected SAR image data.

[0015] Preferably, performing image transformation processing on the multi-view amplitude data to obtain a strong scattering area mask includes: generating a pixel value histogram of the multi-view amplitude data according to the multi-view amplitude data;

[0016] truncating pixel values ​​in the pixel value histogram that are greater than a truncation percentage before the pixel value to 1, and truncate pixel values ​​that are less than the truncation percentage after the pixel value to 0, wherein the truncation percentage is obtained by presetting;

[0017] Normalizing the remaining pixel values ​​in the pixel value histogram to values ​​between 0 and 1;

[0018] The truncated and normalized pixel values ​​are converted into an image to obtain a mask of the strong scattering area.

[0019] Preferably, obtaining, based on the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data, a first overlapping region mask of a ground object target in the digital surface model data in a radar domain coordinate system and the radar domain coordinates of the ground object target, includes:

[0020] constructing a strict geometric positioning model of the image data according to the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data, wherein the strict geometric positioning model includes a distance equation and a Doppler equation;

[0021] Solving the Doppler equation using the Newton iteration method to obtain the azimuth time corresponding to the ground object in the digital surface model data;

[0022] Substituting the azimuth time into the distance equation to obtain the range delay corresponding to the ground object;

[0023] Converting the azimuth time and the range delay into a range index and an azimuth index based on the range resolution, the azimuth resolution, and the reference time;

[0024] The range index and the azimuth index are used as the radar domain coordinates of the ground object target.

[0025] Preferably, obtaining, based on the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data, a first overlapping region mask of a ground object target in the digital surface model data in a radar domain coordinate system and the radar domain coordinates of the ground object target, further comprises:

[0026] Determine, according to the range index and the azimuth index, a radar domain coordinate system grid data point at which each of the ground object targets is located, and count a first number of the ground object targets in each of the radar domain coordinate system grid data points;

[0027] A first overlap degree matrix in a radar domain coordinate system is generated according to the first quantity, and the first overlap degree matrix is ​​binarized to obtain a first overlap area mask of the ground object in the radar domain coordinate system.

[0028] Preferably, determining a second overlapping area mask of the ground object target in a latitude and longitude coordinate system based on the digital surface model data and the radar domain coordinates, and determining the geocoded coordinates and weight of each overlapping pixel point corresponding to the second overlapping area mask, includes:

[0029] Determining, based on the digital surface model data and the radar domain coordinates, a latitude and longitude coordinate system grid data point at which each of the ground object targets is located, and counting a second number of the ground object targets within each of the latitude and longitude coordinate system grid data points;

[0030] generating a second overlap degree matrix in a latitude and longitude coordinate system according to the second quantity, and performing binarization processing on the second overlap degree matrix to obtain a second overlap area mask of the ground object in the latitude and longitude coordinate system;

[0031] Using the determined geocoded coordinates of each grid within the second overlapping area mask as the geocoded coordinates of each overlapping pixel point corresponding to the second overlapping area mask;

[0032] The overlap degree value of each of the overlapping pixels is calculated according to the second overlap degree matrix, and the overlap degree value is used as the weight corresponding to the overlapping pixel.

[0033] Preferably, matching the strong scattering region mask and the first overlapping region mask to obtain an overlapping region between the first overlapping region mask and the strong scattering region mask includes:

[0034] Performing a two-dimensional Fourier transform on the strong scattering area mask to obtain a strong scattering area mask spectrum, and performing a two-dimensional Fourier transform on the first overlapping area mask to obtain a radar domain overlapping mask spectrum;

[0035] Calculating a frequency domain response spectrum according to the strong scattering region mask spectrum and the radar domain overlay mask spectrum;

[0036] Performing a two-dimensional inverse Fourier transform on the frequency domain response spectrum to obtain a time domain response graph;

[0037] A maximum value in the time domain response map is found, and the time domain response map corresponding to the maximum value is used as an overlapping area of ​​the first overlapping area mask and the strong scattering area mask.

[0038] Preferably, constructing the correction amount model of the target area according to each of the geocoded coordinates and the weight includes:

[0039] Inputting the geocoded coordinates into a pre-constructed correction function relationship to obtain the corrected geocoded coordinates;

[0040] Processing the corrected geocoded coordinates and the multi-view amplitude data based on an interpolation operation to obtain a pixel value vector of each of the overlapping pixel points;

[0041] Obtaining correction model parameters based on the constructed weight vector and the pixel value vector;

[0042] The correction amount model is constructed at least according to the correction amount model parameters.

[0043] In a second aspect, the present invention further provides a terrain correction geocoding system for implementing the terrain correction geocoding method described above, the system comprising: a data acquisition and processing unit, an image transformation processing unit, a backward geocoding and overlay mask generation unit, a geocoding coordinate and weight generation unit, a matching unit, a correction amount model construction unit, and a correction processing unit;

[0044] The data acquisition and processing unit is used to obtain digital surface model data of the target area, radar parameter data and image data obtained by the SAR system; perform multi-look processing on the radar parameter data to obtain multi-look radar parameter data, and perform multi-look processing on the image data to obtain multi-look amplitude data;

[0045] The image transformation processing unit is configured to perform image transformation processing on the multi-look amplitude data to obtain a strong scattering area mask; the backward geocoding and overlay mask generation unit is configured to obtain a first overlay area mask of a ground object target in the digital surface model data in a radar domain coordinate system and the radar domain coordinates of the ground object target based on the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data;

[0046] The geocoding coordinate and weight generating unit is configured to determine a second overlapping area mask of the ground object in a latitude and longitude coordinate system based on the digital surface model data and the radar domain coordinates, and determine the geocoding coordinates and weight of each overlapping pixel point corresponding to the second overlapping area mask;

[0047] The matching unit is configured to match the strong scattering region mask with the first overlapping region mask to obtain an overlapping region between the first overlapping region mask and the strong scattering region mask;

[0048] The correction amount model construction unit is configured to construct a correction amount model of the target area according to each of the geocoded coordinates and the weight;

[0049] The correction processing unit is used to correct the overlapping area using the correction amount model to obtain corrected SAR image data.

[0050] In a third aspect, the present invention also provides a computer device, which includes a memory, a processor and a transceiver, which are connected via a bus; the memory is used to store a set of computer program instructions and data, and transmit the stored data to the processor, and the processor executes the program instructions stored in the memory to perform the terrain correction geocoding method described above.

[0051] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium. When the computer program is executed, the above-mentioned L terrain correction geocoding method is implemented.

[0052] The present invention provides a terrain correction geocoding method, system, device, and medium. Compared with the prior art, the embodiments of the present invention have the following advantages:

[0053] (1) Based on the high correlation between the strong scattering area and the overlapping area, the deviation of the geocoded coordinates caused by the radar parameter error is corrected to achieve more accurate terrain correction.

[0054] (2) It does not rely on simulated SAR image algorithms or image matching algorithms. The algorithms used are more mature, simple, and efficient, and therefore have higher adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a schematic diagram of the steps of a terrain correction geocoding method provided by a preferred embodiment of the present invention;

[0056] Figure 2 is a pixel value statistical histogram of multi-view amplitude data provided by a preferred embodiment of the present invention;

[0057] Figure 3 This is a visualization result after histogram truncation and normalization provided by a preferred embodiment of the present invention;

[0058] Figure 4 A strong scattering area mask provided by a preferred embodiment of the present invention;

[0059] Figure 5 A distribution map of geographically coded coordinate points in a radar coordinate system provided by a preferred embodiment of the present invention;

[0060] Figure 6 is a statistical histogram of the first masking degree matrix conversion provided by a preferred embodiment of the present invention;

[0061] Figure 7 1 is a schematic diagram of a mask in a first overlapping region provided by a preferred embodiment of the present invention;

[0062] Figure 8 1 is a schematic diagram of a second overlapping mask region provided by a preferred embodiment of the present invention;

[0063] Figure 9 : is the distribution of overlapping pixels before merging (a) and after merging (b) provided by a preferred embodiment of the present invention;

[0064] Figure 10 Schematic diagram of the degree of overlap between the strong scattering region mask and the first overlapping region mask before matching provided by a preferred embodiment of the present invention;

[0065] Figure 11 Schematic diagram of the overlap degree between the matched strong scattering region mask and the first overlapping region mask provided by a preferred embodiment of the present invention;

[0066] Figure 12 It is a structural diagram of a terrain correction geocoding system provided by a preferred embodiment of the present invention;

[0067] Figure 13 It is a structural diagram of a computer device provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0068] The following is a detailed explanation of the embodiments of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and cannot be understood as limitations on the present invention. The accompanying drawings are for reference and illustration purposes only and do not constitute a limitation on the scope of protection of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the description of the present invention, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, the meaning of "multiple" is two or more.

[0069] In the description of the present invention, it should be noted that, unless otherwise expressly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are for illustrative purposes only, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0070] In describing the present invention, it should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. Those skilled in the art will understand the specific meanings of the above terms in the present invention in specific circumstances.

[0071] In an embodiment of the present invention, a terrain correction geocoding method is provided. Figure 1 , the method comprising:

[0072] S1. Acquire digital surface model data, radar parameter data, and image data acquired by a SAR system of a target area; perform multi-look processing on the radar parameter data to obtain multi-look radar parameter data; and perform multi-look processing on the image data to obtain multi-look amplitude data.

[0073] S2. Perform image transformation processing on the multi-view amplitude data to obtain a strong scattering area mask.

[0074] S3. Obtain, based on the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data, a first overlapping region mask of a ground object target in the digital surface model data in a radar domain coordinate system and radar domain coordinates of the ground object target.

[0075] S4. Determine a second overlapping area mask of the ground object target in a latitude and longitude coordinate system based on the digital surface model data and the radar domain coordinates, and determine the geocoding coordinates and weight of each overlapping pixel point corresponding to the second overlapping area mask.

[0076] S5. Match the strong scattering region mask and the first overlapping region mask to obtain an overlapping region between the first overlapping region mask and the strong scattering region mask.

[0077] S6. Constructing a correction model for the target area according to each of the geocoded coordinates and the weight.

[0078] S7. Correct the overlapping area using the correction model to obtain corrected SAR image data.

[0079] The terrain-corrected geocoding method disclosed in the preferred embodiment of the present invention performs terrain-corrected geocoding based on the high correlation between overlapped areas and strong scattering regions in SAR images. The vast majority of synthetic aperture radar imagery Level 1 products are single-look complex data. This data has the highest original resolution but exhibits significant speckle noise. To reduce the impact of this noise, multi-look processing is required on the single-look complex data. The basic principle of multi-look processing is to average multiple adjacent pixels in the SAR image, thereby reducing speckle noise and improving the signal-to-noise ratio.

[0080] In a preferred embodiment of the present invention, multi-look processing is performed on the acquired radar parameter data to obtain multi-look radar parameter data, and multi-look processing is performed on the image data acquired by the SAR system to obtain multi-look amplitude data. There are two ways to implement multi-look processing, namely time domain multi-look processing and frequency domain multi-look processing. Time domain multi-look processing is implemented in the time domain, and frequency domain multi-look processing is implemented in the frequency domain.

[0081] The time domain multi-view processing includes mean filtering processing and downsampling processing, wherein the width of the mean filtering window of the mean filtering processing is equal to the number of range views, and the height of the mean filtering window of the mean filtering processing is equal to the number of azimuth views.

[0082] Frequency-domain multi-look processing involves a 2D Fourier transform, spectrum partitioning, an inverse 2D Fourier transform, and mean calculation. A 2D Fourier transform is performed on the radar parameter data and image data. The spectrum is then divided into several sub-spectra based on the specified number of range and azimuth looks. Each sub-spectra is then inversely transformed to obtain time-domain images of the sub-spectra. The pixel-by-pixel mean of these sub-spectra time-domain images is then calculated, ultimately yielding frequency-domain multi-look radar parameter data and multi-look amplitude data. After multi-look processing, these data only contain amplitude information, reducing image resolution but effectively suppressing speckle noise.

[0083] Furthermore, the multi-look amplitude data is transformed to obtain a strong scattering area mask. Specifically, a pixel value histogram of the multi-look amplitude data is generated based on the multi-look amplitude data. The physical meaning of the multi-look amplitude data is the sum of the backscattered waves from all ground objects within the radar resolution unit. Strong scattering areas mainly come from two sources: one is from ground objects with high backscatter coefficients, such as some buildings and corner reflectors; the other is from areas with steep terrain. Due to the side-looking imaging mechanism of the SAR system, these areas are subject to perspective contraction and overlap, resulting in the superposition of scattered waves from multiple ground objects within a radar resolution unit, which in turn appears as high pixel values ​​in the image. Therefore, the strong scattering area mask actually includes both areas with high backscatter coefficients and the location information of overlapped areas in the image generated by the SAR system.

[0084] The pixel values ​​of multi-view amplitude data have a large dynamic range, such as Figure 2 The figure shows the pixel value statistical histogram of multi-view amplitude data. Figure 2 It can be seen that most of the pixel values ​​of the multi-view amplitude data are concentrated in the range of lower pixel values. To facilitate the visualization of the multi-view amplitude data, the pixel value statistical histogram of the multi-view amplitude data is truncated and stretched. Specifically, the pixel values ​​in the pixel value histogram that are greater than the pixel value before the truncation percentage are truncated to 1, and the pixel values ​​that are less than the pixel value after the truncation percentage are truncated to 0. The truncation percentage is obtained by pre-setting. In this application, the truncation percentage is 2.5%. Figure 2 As shown by the dotted line, Figure 2 The red part is cut off. The remaining pixel values ​​in the pixel value histogram are normalized to values ​​between 0 and 1. Figure 2 The pixel values ​​in the blue part are normalized to values ​​between 0 and 1. The truncated and normalized pixel values ​​are converted into an image, such as Figure 3 The following is the visualization result after histogram truncation and normalization, and the strong scattering area mask is obtained based on the visualization result. In the preferred embodiment of the present invention, pixels with values ​​greater than the top 2.5% of the pixels are regarded as strong scattering area pixels, and the strong scattering area mask can be obtained, such as Figure 4 shown.

[0085] Furthermore, based on the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data, the first overlay region mask and the radar domain coordinates of the ground object in the digital surface model data in the radar domain coordinate system are obtained. Specifically, first, based on the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data, a backward geocoding algorithm is used to determine the row and column numbers of any raster data point in the digital surface model data in the multi-look amplitude data, i.e., the directional index and the range index.

[0086] Before performing the backward geocoding algorithm, the DSM data must be preprocessed. This includes elevation datum conversion, DSM resampling, and conversion of geodetic coordinates to Earth-centered, Earth-fixed coordinates. Because different DSMs have different elevation datums, to avoid introducing elevation errors, the DSM data's elevation datum must be uniformly converted to the WGS84 ellipsoidal high datum. The DSM data is then resampled to the same resolution as the multi-view amplitude data. Finally, the geodetic coordinates (longitude, latitude, and altitude) of each grid data point in the DSM data are converted to ECEF coordinates (x, y, and z) to serve as input to the backward geocoding algorithm.

[0087] The backward geocoding algorithm needs to construct a strict geometric positioning model for multi-view amplitude data, namely the range-Doppler model. The range-Doppler model includes the distance equation and the Doppler equation, as shown below:

[0088]

[0089] Where c is the speed of light, λ is the wavelength of the radar carrier, τ rg is the distance delay, t az is the azimuth time of Doppler moment, (X T , Y T , Z T ) is the Earth-centered Earth-fixed coordinate of the ground object, (X s , Y s , Z s ) is the Earth-centered, Earth-fixed coordinate of the SAR sensor platform at the Doppler time, (V X , V Y , V Z ) is the velocity of the SAR sensor platform at the Doppler moment, f D is the Doppler frequency.

[0090] The Doppler equation, the second equation in the range-Doppler model, contains only one unknown parameter, t, which is the azimuth time. az , use Newton iteration method to solve this one-variable nonlinear equation, and then solve the azimuth time t corresponding to the ground object target az .

[0091] Direction to time t az Substitute the solution of into the range equation, that is, the first equation in the range Doppler model equation group, and the range delay τ corresponding to the ground object target can be solved. rg .

[0092] Finally, according to the resolution in range and azimuth (Δτ, Δt) and the reference time (τ rg0 , t az0 ), convert the range delay and azimuth time into the range index Irg and azimuth index I az , the conversion formula is:

[0093]

[0094] Finally, the range index and azimuth index are used as the radar domain coordinates of the ground object.

[0095] Furthermore, based on the range index and azimuth index, the first overlay area mask of the ground object in the digital surface model data in the radar domain coordinate system is obtained. The physical meaning of the overlay mask is that a radar resolution unit superimposes the scattered echoes of multiple ground objects, which is expressed in the geocoding coordinates as a grid in the radar image containing the geocoding coordinate points of multiple ground objects. Figure 5 The figure shows the distribution of geocoded coordinate points in the radar coordinate system. Figure 5 It can be seen that the denser the geocoded coordinate points in the radar coordinate system, the higher the degree of overlap in the area, which often corresponds to a higher pixel value in the image data acquired by the SAR system. In the present invention, the degree of overlap is measured by counting the number of geocoded coordinate points falling within the same grid. Specifically, the radar domain coordinate grid where each ground object is located is determined based on the range index and the azimuth index, and the first number of ground object targets within each radar domain coordinate grid is counted. Moreover, the longitude and latitude coordinate grid where each ground object is located is determined based on the digital surface model data and the radar domain coordinates, and the second number of ground object targets within each longitude and latitude coordinate grid is counted.

[0096] Specifically, remember N rg N is the number of grids in the range direction of the multi-view amplitude image. az N is the number of grids in the azimuth direction of the multi-view amplitude image. lat N is the number of grids in the latitude direction after the digital surface model data is resampled in DSM. lon is the number of grids in the longitude direction after the DSM resampling of the digital surface model data, and the overlap degree matrix L in the radar coordinate system is initialized to zero. s (N az , N rg ) is an all-zero matrix, and the overlap degree matrix Q in the longitude and latitude coordinate system is -1·ones(N lat , N lon ) is a constant matrix.

[0097] Traverse the geocoded coordinates of all raster data points (i, j) after the digital surface model data is resampled in DSM When the geocoded coordinates meet and When executing:

[0098]

[0099] L[y,x]=L[y,x]+1

[0100] Q[i,j]=x+y·N rg

[0101] Where round represents rounding, x represents the integer value of the geocoded distance coordinate of the raster data point (i, j), y represents the integer value of the geocoded azimuth coordinate of the raster data point (i, j), L[y, x] represents the element value in the y-th row and x-th column of the matrix L, and Q[i, j] represents the element value in the i-th row and j-th column of the matrix Q.

[0102] Furthermore, each grid data point (i, j) of the overlap degree matrix Q in the longitude and latitude coordinate system is traversed. If Q[i, j] is equal to -1, Q[i, j] is set to 0; otherwise, the following is executed:

[0103] y=floor(Q[i,j] / N rg )

[0104] x=Q[i,j]-y·N rg

[0105] Q[i,j]=L[y,x]

[0106] Among them, floor represents the rounding down operation.

[0107] Through the above statistical operations, we can obtain the first overlap degree matrix L in the radar domain coordinate system and the second overlap degree matrix Q in the longitude and latitude coordinate system. The first overlap degree matrix is ​​used to obtain the first overlap area mask of the ground object in the radar domain coordinate system. Specifically, the first overlap degree matrix is ​​converted into a statistical histogram, as shown in Figure 6 As shown, a binarization threshold is determined. In the preferred embodiment of the present application, the binarization threshold is 10%. Pixels with an overlap greater than the previous binarization threshold are marked as overlap areas, and the first overlap area mask of the ground object target in the radar domain coordinate system is obtained, as shown in FIG. Figure 7 Shown is a schematic diagram of the mask in the first overlapping mask area.

[0108] The second overlap degree matrix Q in the longitude and latitude coordinate system is used to determine the second overlap area mask of the ground object in the longitude and latitude coordinate system, and to determine the geocoding coordinates and weight of each overlapped pixel point corresponding to the second overlap area mask. Specifically, the second overlap degree matrix is ​​converted into a statistical histogram, and pixels with an overlap degree greater than the pre-binarization threshold are marked as overlap areas, and the second overlap area mask of the ground object in the radar domain coordinate system is obtained, as shown in FIG. Figure 8The figure shows a schematic diagram of the second overlapping area mask. The geocoded coordinates of each grid cell within the second overlapping area mask are determined and used as the geocoded coordinates of each overlapping pixel corresponding to the second overlapping area mask. Based on the second overlapping degree matrix, the overlapping degree value of each overlapping pixel is calculated and used as the weight of the corresponding overlapping pixel. The weight calculation formula is as follows:

[0109]

[0110] Among them, N represents the total number of overlapping pixels, Q i Indicates the overlap degree value of the i-th overlapped pixel, W i Represents the weight of the i-th overlapped pixel.

[0111] Furthermore, for areas with a high degree of overlap, a radar resolution unit often contains multiple adjacent overlapping pixels. Due to their close locations, the pixel values ​​of the radar images corresponding to these adjacent overlapping pixels are approximately equal. A distance threshold is set, and overlapping pixels whose distances are less than the distance threshold are merged into one equivalent point. Figure 9 The figure shows the distribution of overlapping pixels before (a) and after (b) merging. The brightness of the tones reflects the weight differences of the overlapping pixels. The geocoded coordinates of the equivalent point are the mean of the coordinates of its adjacent overlapping pixels, and the weight is the cumulative sum of the weights of its adjacent overlapping pixels. After merging, the amount of data processed is reduced, thereby improving the efficiency of the algorithm.

[0112] Furthermore, the strong scattering area mask and the first overlapping area mask are matched to obtain the overlapping area of ​​the first overlapping area mask and the strong scattering area mask. Since the first overlapping area mask is highly correlated with the strong scattering area mask, when the two masks are matched, the correlation is the largest. The strong scattering area mask is denoted as M. S , the first overlapping area mask is M L , use the frequency domain method to realize the correlation operation of the two masks. Specifically, determine the search range of the rough matching distance and azimuth according to the prior knowledge; respectively, S and M L The two masks are padded with zeros until the size satisfies the range and azimuth search ranges; then the strong scattering area mask is subjected to a two-dimensional Fourier transform to obtain the strong scattering area mask spectrum F s , perform a two-dimensional Fourier transform on the first overlapping area mask to obtain the radar domain overlapping mask spectrum F L , specifically expressed as follows:

[0113] F S =fft2(M s )

[0114] F L =fft2(ML )

[0115] Where fft2 represents two-dimensional Fourier transform.

[0116] Furthermore, the frequency domain response spectrum is calculated based on the strong scattering area mask spectrum and the radar domain overlay mask spectrum. The calculation formula is:

[0117]

[0118] Among them, F R Represents the frequency domain response spectrum.

[0119] Furthermore, the frequency domain response spectrum is subjected to a two-dimensional inverse Fourier transform to obtain a time domain response diagram, which is specifically expressed as follows:

[0120] R=ifft2(F R )

[0121] Where R represents the time domain response diagram, and ifft2 represents the two-dimensional inverse Fourier transform.

[0122] Furthermore, the maximum value in the time domain response map is found, and the time domain response map corresponding to the maximum value is used as the overlapping area of ​​the first overlapping area mask relative to the strong scattering area mask.

[0123] like Figure 10 As shown in FIG, it is a schematic diagram of the overlap degree between the strong scattering area mask and the first overlapping area mask before matching, as shown in FIG. Figure 11 The figure below shows the overlap between the matched strong scattering region mask and the first overlay region mask. Yellow pixels represent the strong scattering region mask, blue pixels represent the first overlay region mask, and white pixels represent the overlap between the two. It can be observed that the strong scattering region mask and the first overlay region mask have many regions with similar geometric structures, indicating a high correlation between the overlay region and the strong scattering region. After matching using the above method, the overlap between the two masks is significantly increased.

[0124] Furthermore, a correction model for the target area is constructed based on each geocoded coordinate and the weights. During Earth observation, the SAR system is affected by multiple error sources. Some error sources only affect one dimension, either range or azimuth. For example, atmospheric propagation delay only affects range delay, and satellite ephemeris error only affects azimuth time. Other error sources affect both range and azimuth dimensions, resulting in spatial variability within the imaging scene. Therefore, in the present invention, polynomials related to range and azimuth indices are used to fit the deviations of geocoded coordinates caused by various error sources. In a preferred embodiment of the present application, the correction function is expressed as:

[0125] f rg(x,y)=a0+a1x+a2y

[0126] f az (x,y)=b0+bx+b2y

[0127] Where (x, y) represents the geocoded coordinates before correction, f rg (x, y) represents the distance correction of the geocoded coordinates of the overlapped pixel point, f az (x, y) represents the azimuth correction of the geocoded coordinates of the overlapped pixel point, a i 、b i (i=0,1,2) represents the coefficients of the polynomial to be solved.

[0128] The geocoded coordinates of each overlapping pixel point corresponding to the second overlapping area mask are input into the correction function relationship to obtain the corrected geocoded coordinates, which can be specifically expressed as:

[0129] x′=x+f rg (x,y)

[0130] y'=y+f az (x,y)

[0131] Where (x′, y′) represents the geocoded coordinates before correction.

[0132] Furthermore, the corrected geocoded coordinates and multi-view amplitude data are processed based on interpolation operations to obtain the pixel value vector γ of each overlapping pixel point = [DN i ](1≤i≤N).

[0133] Furthermore, according to the weight of each overlapping pixel point corresponding to the second overlapping area mask, a weight vector w=[W i ](1≤i≤N).

[0134] Because the degree of overlap is highly correlated with scattered wave intensity, mathematically expressed as a high inner product between vectors γ and w. If a set of polynomial coefficients can be found that maximizes the inner product between the pixel value vector of the overlapped pixel corrected by the correction model and its weight vector, the deviation in geocoded coordinates caused by the error can be considered fully corrected. Therefore, the process of fitting the correction model can be transformed into the following optimization problem:

[0135]

[0136] in, and are the polynomial coefficients of the correction model to be optimized.

[0137] Solving the above optimization function involves multiple interpolation operations, which is not conducive to gradient calculation. Therefore, the present invention uses a metaheuristic optimization algorithm, such as particle swarm optimization, to solve it. Using a metaheuristic optimization algorithm can avoid gradient calculations and has stronger global search capabilities, avoiding getting stuck in local optimal solutions, and ultimately obtaining the corrected model parameters.

[0138] After obtaining the correction amount model parameters, a correction amount model is constructed at least according to the correction amount model parameters.

[0139] Finally, the correction amount model is used to correct the matched overlapping area to obtain the corrected SAR image data.

[0140] In a preferred embodiment of the present invention, digital surface model data, radar parameter data, and image data acquired by a SAR system are obtained for a target area; multi-look processing is performed on the radar parameter data to obtain multi-look radar parameter data, and multi-look processing is performed on the image data to obtain multi-look amplitude data; image transformation processing is performed on the multi-look amplitude data to obtain a strong scattering area mask; a first overlay area mask of a ground object target in the digital surface model data in a radar domain coordinate system and radar domain coordinates of the ground object target are obtained based on the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data; a second overlay area mask of the ground object target in a latitude and longitude coordinate system is determined based on the digital surface model data and the radar domain coordinates, and the geocoded coordinates and weight of each overlay pixel point corresponding to the second overlay area mask are determined; the strong scattering area mask is matched with the first overlay area mask to obtain an overlapping area between the first overlay area mask and the strong scattering area mask; a correction model for the target area is constructed based on each geocoded coordinate and weight; and the correction model is used to correct the overlapping area to obtain corrected SAR image data. The terrain correction geocoding method provided in this application corrects the deviation of geocoding coordinates caused by radar parameter errors based on the high correlation between strong scattering areas and overlapping areas, thereby achieving more accurate terrain correction. It does not rely on simulated SAR image algorithms or image matching algorithms. The algorithms used are more mature, simple, and efficient, and therefore have higher adaptability.

[0141] Accordingly, if Figure 12 As shown, based on a terrain correction geocoding method, an embodiment of the present invention further provides a terrain correction geocoding system to implement the terrain correction geocoding method disclosed in an embodiment of the present invention. The system includes: a data acquisition and processing unit 1, an image transformation processing unit 2, a backward geocoding and overlay mask generation unit 3, a geocoding coordinate and weight generation unit 4, a matching unit 5, a correction amount model construction unit 6, and a correction processing unit 7;

[0142] The data acquisition and processing unit 1 is configured to acquire digital surface model data of the target area, radar parameter data, and image data acquired by the SAR system; perform multi-look processing on the radar parameter data to obtain multi-look radar parameter data; and perform multi-look processing on the image data to obtain multi-look amplitude data;

[0143] The image transformation processing unit 2 is used to perform image transformation processing on the multi-view amplitude data to obtain a strong scattering area mask;

[0144] The backward geocoding and overlay mask generation unit 3 is configured to obtain a first overlay region mask of a ground object in the digital surface model data in a radar domain coordinate system and radar domain coordinates of the ground object in the digital surface model data based on the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data;

[0145] The geocoding coordinate and weight generating unit 4 is configured to determine a second overlapping area mask of the ground object in a latitude and longitude coordinate system based on the digital surface model data and the radar domain coordinates, and determine the geocoding coordinates and weight of each overlapping pixel point corresponding to the second overlapping area mask;

[0146] The matching unit 5 is configured to match the strong scattering region mask with the first overlapping region mask to obtain an overlapping region between the first overlapping region mask and the strong scattering region mask;

[0147] The correction amount model construction unit 6 is used to construct a correction amount model of the target area according to each of the geocoded coordinates and the weight;

[0148] The correction processing unit 7 is used to correct the overlapping area using the correction amount model to obtain corrected SAR image data.

[0149] For the specific definition of a terrain correction geocoding system, please refer to the above-mentioned definition of a terrain correction geocoding method, which will not be repeated here. A person of ordinary skill in the art will appreciate that the modules and steps described in conjunction with the embodiments disclosed in the present invention can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0150] like Figure 13As shown, an embodiment of the present invention provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the steps in the above-mentioned terrain correction geocoding method embodiment are implemented, for example Figure 1 Steps S1 to S7 described in .

[0151] Those skilled in the art will understand that the schematic Figure 13 These are merely examples of computer devices and do not constitute limitations on the computer device. The computer device may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0152] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the computer device, connecting various parts of the entire computer device using various interfaces and lines.

[0153] The memory can be used to store the computer programs and / or modules, and the processor implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0154] Wherein, if the module integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0155] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0156] Accordingly, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to perform the steps in the terrain correction geocoding method of the above embodiment, for example Figure 1 Steps S1 to S7 described in .

[0157] The terrain correction geocoding method, system, device, and medium provided in this embodiment are used to solve the technical problem of designing a terrain correction geocoding method to improve universality. The method includes obtaining digital surface model data, radar parameter data, and image data acquired by a SAR system for a target area; performing multi-look processing on the radar parameter data to obtain multi-look radar parameter data, and performing multi-look processing on the image data to obtain multi-look amplitude data; performing image transformation processing on the multi-look amplitude data to obtain a strong scattering area mask; obtaining a first overlay area mask of the ground object target in the digital surface model data in a radar domain coordinate system and the radar domain coordinates of the ground object target based on the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data; determining a second overlay area mask of the ground object target in a latitude and longitude coordinate system based on the digital surface model data and the radar domain coordinates, and determining the geocoding coordinates and weights of each overlay pixel point corresponding to the second overlay area mask; matching the strong scattering area mask with the first overlay area mask to obtain an overlapping area between the first overlay area mask and the strong scattering area mask; constructing a correction model for the target area based on each geocoding coordinate and weight; and correcting the overlapping area using the correction model to obtain corrected SAR image data. The terrain correction geocoding method provided in this application corrects the deviation of geocoding coordinates caused by radar parameter errors based on the high correlation between strong scattering areas and overlapping areas, thereby achieving more accurate terrain correction. It does not rely on simulated SAR image algorithms or image matching algorithms. The algorithms used are more mature, simple, and efficient, and therefore have higher adaptability.

[0158] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0159] The above-described embodiments merely represent several preferred embodiments of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that a person skilled in the art could make several improvements and substitutions without departing from the technical principles of the present invention, and such improvements and substitutions should also be considered within the scope of the present invention. Therefore, the scope of the present invention should be determined by the scope of the claims.

Claims

1. A terrain correction geocoding method, characterized in that: The method comprises: Acquire digital surface model data of the target area, radar parameter data, and image data acquired by a SAR system; perform multi-look processing on the radar parameter data to obtain multi-look radar parameter data, and perform multi-look processing on the image data to obtain multi-look amplitude data; Performing image transformation processing on the multi-view amplitude data to obtain a strong scattering area mask; Obtaining, based on the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data, a first overlapping region mask of a ground object in the digital surface model data in a radar domain coordinate system and radar domain coordinates of the ground object; Determining a second overlapping area mask of the ground object in a latitude and longitude coordinate system based on the digital surface model data and the radar domain coordinates, and determining the geocoding coordinates and weight of each overlapping pixel point corresponding to the second overlapping area mask; Matching the strong scattering region mask and the first overlapping region mask to obtain an overlapping region between the first overlapping region mask and the strong scattering region mask; constructing a correction amount model for the target area according to each of the geocoded coordinates and the weight; Correcting the overlapping area using the correction model to obtain corrected SAR image data; The step of constructing a correction model for the target area according to each of the geocoded coordinates and the weight includes: Inputting the geocoded coordinates into a pre-constructed correction function relationship to obtain the corrected geocoded coordinates; Processing the corrected geocoded coordinates and the multi-view amplitude data based on an interpolation operation to obtain a pixel value vector of each of the overlapping pixel points; Obtaining correction model parameters based on the constructed weight vector and the pixel value vector; The correction amount model is constructed at least according to the correction amount model parameters.

2. The terrain correction geocoding method according to claim 1, wherein: The performing image transformation processing on the multi-view amplitude data to obtain a strong scattering area mask includes: generating a pixel value histogram of the multi-view amplitude data according to the multi-view amplitude data; truncating pixel values ​​in the pixel value histogram that are greater than a truncation percentage before the pixel value to 1, and truncate pixel values ​​that are less than the truncation percentage after the pixel value to 0, wherein the truncation percentage is obtained by presetting; Normalizing the remaining pixel values ​​in the pixel value histogram to values ​​between 0 and 1; The truncated and normalized pixel values ​​are converted into an image to obtain a mask of the strong scattering area.

3. The terrain correction geocoding method according to claim 1, wherein: The obtaining, based on the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data, a first overlapping region mask of a ground object target in the digital surface model data in a radar domain coordinate system and radar domain coordinates of the ground object target, includes: constructing a strict geometric positioning model of the image data according to the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data, wherein the strict geometric positioning model includes a distance equation and a Doppler equation; Solving the Doppler equation using the Newton iteration method to obtain the azimuth time corresponding to the ground object in the digital surface model data; Substituting the azimuth time into the distance equation to obtain the range delay corresponding to the ground object; Converting the azimuth time and the range delay into a range index and an azimuth index based on the range resolution, the azimuth resolution, and the reference time; The range index and the azimuth index are used as the radar domain coordinates of the ground object target.

4. The terrain correction geocoding method according to claim 3, wherein: The step of obtaining, based on the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data, a first overlapping region mask of a ground object target in the digital surface model data in a radar domain coordinate system and the radar domain coordinates of the ground object target further includes: Determine, according to the range index and the azimuth index, a radar domain coordinate system grid data point at which each of the ground object targets is located, and count a first number of the ground object targets in each of the radar domain coordinate system grid data points; A first overlap degree matrix in a radar domain coordinate system is generated according to the first quantity, and the first overlap degree matrix is ​​binarized to obtain a first overlap area mask of the ground object in the radar domain coordinate system.

5. The terrain correction geocoding method according to claim 4, wherein: The step of determining a second overlapping region mask of the ground object in a latitude and longitude coordinate system based on the digital surface model data and the radar domain coordinates, and determining the geocoded coordinates and weight of each overlapping pixel point corresponding to the second overlapping region mask, includes: Determining, based on the digital surface model data and the radar domain coordinates, a latitude and longitude coordinate system grid data point at which each of the ground object targets is located, and counting a second number of the ground object targets within each of the latitude and longitude coordinate system grid data points; generating a second overlap degree matrix in a latitude and longitude coordinate system according to the second quantity, and performing binarization processing on the second overlap degree matrix to obtain a second overlap area mask of the ground object in the latitude and longitude coordinate system; Using the determined geocoded coordinates of each grid within the second overlapping area mask as the geocoded coordinates of each overlapping pixel point corresponding to the second overlapping area mask; The overlap degree value of each of the overlapping pixels is calculated according to the second overlap degree matrix, and the overlap degree value is used as the weight corresponding to the overlapping pixel.

6. The terrain correction geocoding method according to claim 5, wherein: The matching of the strong scattering region mask and the first overlapping region mask to obtain an overlapping region between the first overlapping region mask and the strong scattering region mask includes: Performing a two-dimensional Fourier transform on the strong scattering area mask to obtain a strong scattering area mask spectrum, and performing a two-dimensional Fourier transform on the first overlapping area mask to obtain a radar domain overlapping mask spectrum; Calculating a frequency domain response spectrum according to the strong scattering region mask spectrum and the radar domain overlay mask spectrum; Performing a two-dimensional inverse Fourier transform on the frequency domain response spectrum to obtain a time domain response graph; A maximum value in the time domain response map is found, and the time domain response map corresponding to the maximum value is used as an overlapping area of ​​the first overlapping area mask and the strong scattering area mask.

7. A terrain correction geocoding system, implementing the terrain correction geocoding method according to any one of claims 1 to 6, characterized in that: The system comprises: a data acquisition and processing unit, an image transformation processing unit, a backward geocoding and overlay mask generation unit, a geocoding coordinate and weight generation unit, a matching unit, a correction amount model construction unit and a correction processing unit; The data acquisition and processing unit is used to obtain digital surface model data of the target area, radar parameter data and image data obtained by the SAR system; perform multi-look processing on the radar parameter data to obtain multi-look radar parameter data, and perform multi-look processing on the image data to obtain multi-look amplitude data; The image transformation processing unit is used to perform image transformation processing on the multi-view amplitude data to obtain a strong scattering area mask; The backward geocoding and overlay mask generation unit is configured to obtain, based on the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data, a first overlay region mask of a ground object in the digital surface model data in a radar domain coordinate system and the radar domain coordinates of the ground object; The geocoding coordinate and weight generating unit is configured to determine a second overlapping area mask of the ground object in a latitude and longitude coordinate system based on the digital surface model data and the radar domain coordinates, and determine the geocoding coordinates and weight of each overlapping pixel point corresponding to the second overlapping area mask; The matching unit is configured to match the strong scattering region mask with the first overlapping region mask to obtain an overlapping region between the first overlapping region mask and the strong scattering region mask; The correction amount model construction unit is configured to construct a correction amount model of the target area according to each of the geocoded coordinates and the weight; The correction processing unit is used to correct the overlapping area using the correction amount model to obtain corrected SAR image data; The step of constructing a correction model for the target area according to each of the geocoded coordinates and the weight includes: Inputting the geocoded coordinates into a pre-constructed correction function relationship to obtain the corrected geocoded coordinates; Processing the corrected geocoded coordinates and the multi-view amplitude data based on an interpolation operation to obtain a pixel value vector of each of the overlapping pixel points; Obtaining correction model parameters based on the constructed weight vector and the pixel value vector; The correction amount model is constructed at least according to the correction amount model parameters.

8. A computer device, characterized in that: The computer device includes a memory, a processor and a transceiver, which are connected via a bus; the memory is used to store a set of computer program instructions and data, and transmit the stored data to the processor, and the processor executes the program instructions stored in the memory to perform the terrain correction geocoding method according to any one of claims 1 to 6.

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, the terrain correction geocoding method according to any one of claims 1 to 6 is implemented.

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