Terrain correction geocoding method, system, equipment and medium

Abstract

The invention discloses a terrain correction geocoding method, system and device and a medium, and the method comprises the steps: carrying out the image transformation processing of obtained multi-view amplitude data, and obtaining a strong scattering region mask; according to the obtained digital earth surface model data, multi-view radar parameter data and multi-view amplitude data, obtaining a first overlay area mask and radar domain coordinates of a ground object target in a radar domain coordinate system and a second overlay area mask of the ground object target in a longitude and latitude coordinate system in the digital earth surface model data; the geocoding coordinate and the weight of each overlay pixel point corresponding to the mask in the second overlay area are obtained; matching the strong scattering area mask with the first overlay area mask to obtain an overlapping area; and correcting the overlapping region by adopting a correction quantity model constructed according to the geocoding coordinates and the weights to obtain corrected image data. The method provided by the invention is more accurate in correction, does not depend on a simulation SAR image algorithm or an image matching algorithm, and has higher adaptive capacity.

Description

Technical Field

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

[0002] Synthetic Aperture Radar (SAR), as an active microwave imaging sensor, has the advantages of all-weather, all-day, large-scale, and strong penetrability, and has played a very important role in geological mapping, land resource survey, environmental disaster monitoring and other fields. However, due to the side-looking imaging geometry of the SAR system, the obtained SAR squint image is prone to geometric distortions such as foreshortening and top-bottom inversion due to the terrain undulation of the observation scene, which is not conducive to people's discrimination of ground object targets; on the other hand, the SAR squint image lacks geographical location information, making its application scenarios more limited. Therefore, before applying SAR images in practice, it is necessary to convert the squint image into an orthoimage through Geocoded TerrainCorrected (GTC) technology to eliminate or weaken the distortion phenomenon caused by side-looking imaging and complex terrain in the SAR image, make it have a real geographical location, and reflect the real surface information.

[0003] However, the SAR system will be affected by various error sources during the ground observation process, such as: internal noise of the sensor, platform orbit positioning error, atmospheric propagation delay of the echo signal, ground elevation error, etc., and it is necessary to correct the image data obtained by the SAR system to improve the accuracy. To improve the accuracy of terrain correction geocoding, the following three improved terrain correction geocoding methods are adopted in the prior art: Method 1: Based on the known high-precision control points in the geometric calibration field, perform adjustment processing on the positioning model to obtain more accurate radar parameters; Method 2: Simulate the SAR squint image of the imaging scene with the help of a Digital Surface Model (DSM), and perform image matching between the simulated SAR squint image and the real SAR squint image to refine the geocoding look-up table; Method 3: Select the optical orthoimage of the same scene as the reference image, perform image matching between the optical orthoimage and the SAR orthoimage, and directly correct the geographical location of the SAR orthoimage to the optical reference image.

[0004] Method 1 has strict requirements on the number, distribution, accuracy, etc. of control points. It is difficult to obtain reliable control points in some extreme terrains. Therefore, the applicable scenario range of Solution 1 is limited. Both Method 2 and Method 3 rely on the heterologous image matching algorithm. The heterologous image matching algorithm needs to have the ability to extract homologous point pairs with reasonable distribution and high registration accuracy between simulated SAR images and real SAR images, which has high requirements for the matching algorithm and limited adaptability to traditional matching algorithms. As a result, the applicable ranges of Method 2 and Method 3 are also relatively limited.

[0005] Therefore, 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

[0006] 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, and achieve the effect of improving the universality of terrain correction geocoding.

[0007] In the first aspect, the present invention provides a terrain correction geocoding method, and the method includes: obtaining digital surface model data, radar parameter data of a target area, and image data obtained 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; performing image transformation processing on the multi-look amplitude data to obtain a strong scattering area mask; According to the digital surface model data, the multi-look radar parameter data and the multi-look amplitude data, obtaining a first superposition mask area mask of a ground object target in the radar domain coordinate system in the digital surface model data and the radar domain coordinates of the ground object target; according to the digital surface model data and the radar domain coordinates, determining a second superposition mask area mask of the ground object target in the longitude and latitude coordinate system, and determining the geocoding coordinates and weights of each superposition pixel point corresponding to the second superposition mask area mask; matching the strong scattering area mask and the first superposition mask area mask to obtain an overlapping area of the first superposition mask area mask and the strong scattering area mask; constructing a correction amount model of the target area according to each geocoding coordinate and the weight; using the correction amount model to correct the overlapping area to obtain corrected SAR image data.

[0008] Preferably, the performing image transformation processing on the multi-look amplitude data to obtain a strong scattering area mask includes: generating a pixel value histogram of the multi-look amplitude data according to the multi-look amplitude data; Truncate the pixel values in the pixel value histogram that are greater than the pre-truncation percentage of the pixel value to 1, and truncate the pixel values that are less than the post-truncation percentage of the pixel value to 0, where the truncation percentage is obtained through presetting; Normalize the remaining pixel values in the pixel value histogram to values between 0 and 1; Convert the truncated and normalized pixel values into an image to obtain a strong scattering region mask.

[0009] Preferably, the obtaining of the first overlapping mask region mask of the ground object in the radar domain coordinate system and the radar domain coordinates of the ground object from the digital terrain model data, the multi-look radar parameter data, and the multi-look amplitude data includes: Construct a strict geometric positioning model for the image data according to the digital terrain model data, the multi-look radar parameter data, and the multi-look amplitude data, where the strict geometric positioning model includes a range equation and a Doppler equation; Solve the Doppler equation using the Newton iteration method to obtain the azimuth time corresponding to the ground object in the digital terrain model data; Substitute the azimuth time into the range equation to obtain the range delay corresponding to the ground object; Based on the range resolution, azimuth resolution, and reference time, convert the azimuth time and the range delay into a range index and an azimuth index; Use the range index and the azimuth index as the radar domain coordinates of the ground object.

[0010] Preferably, the obtaining of the first overlapping mask region mask of the ground object in the radar domain coordinate system and the radar domain coordinates of the ground object from the digital terrain model data, the multi-look radar parameter data, and the multi-look amplitude data further includes: Determine the grid data points in the radar domain coordinate system where each ground object is located according to the range index and the azimuth index, and count the first quantity of the ground objects in each grid data point in the radar domain coordinate system; Generate a first overlapping degree matrix in the radar domain coordinate system according to the first quantity, and perform binarization processing on the first overlapping degree matrix to obtain the first overlapping mask region mask of the ground object in the radar domain coordinate system.

[0011] Preferably, the determining of the second overlapping mask region mask of the ground object in the latitude and longitude coordinate system and the determination of the geographic coding coordinates and weights of each overlapping pixel point corresponding to the second overlapping mask region mask according to the digital terrain model data and the radar domain coordinates includes: Based on the digital surface model data and the radar domain coordinates, determine the grid data points of the geodetic coordinate system where each of the ground object targets is located, and count the second quantity of the ground object targets within each of the grid data points of the geodetic coordinate system; Generate a second layover degree matrix in the geodetic coordinate system according to the second quantity, and perform binarization processing on the second layover degree matrix to obtain a second layover area mask of the ground object targets in the geodetic coordinate system; Take the geographic coding coordinates of each of the grids determined to be within the second layover area mask as the geographic coding coordinates of each layover pixel point corresponding to the second layover area mask; According to the second layover degree matrix, calculate the layover degree value of each layover pixel point, and take the layover degree value as the weight corresponding to the layover pixel point.

[0012] Preferably, the matching of the strong scattering area mask and the first layover area mask to obtain the overlapping area of the first layover area mask and the strong scattering area mask includes: Perform a two-dimensional Fourier transform on the strong scattering area mask to obtain a strong scattering area mask spectrum, and perform a two-dimensional Fourier transform on the first layover area mask to obtain a radar domain layover mask spectrum; Calculate a frequency domain response spectrum according to the strong scattering area mask spectrum and the radar domain layover mask spectrum; Perform an inverse two-dimensional Fourier transform on the frequency domain response spectrum to obtain a time domain response map; Find the maximum value in the time domain response map, and take the time domain response map corresponding to the maximum value as the overlapping area of the first layover area mask and the strong scattering area mask.

[0013] Preferably, the constructing the correction amount model of the target area according to each of the geographic coding coordinates and the weight includes: Input the geographic coding coordinates into a pre-constructed correction amount function relational expression to obtain corrected geographic coding coordinates; Process the corrected geographic coding coordinates and the multi-look amplitude data based on interpolation operation to obtain a pixel value vector of each layover pixel point; Based on the constructed weight vector and the pixel value vector, obtain correction amount model parameters; Construct the correction amount model at least according to the correction amount model parameters.

[0014] Second aspect, the present invention further provides a terrain correction geocoding system for implementing the terrain correction geocoding method described above. The system includes: a data acquisition and processing unit, an image transformation and processing unit, a backward geocoding and layover 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 configured to obtain digital surface model data of a target area, radar parameter data, and image data acquired through an 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 and 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 layover mask generation unit is configured to obtain a first layover area mask of a ground object in the radar domain coordinate system in the digital surface model data and the radar domain coordinates of the ground object according to the digital surface model data, the multi-look radar parameter data, and the multi-look amplitude data; The geocoding coordinate and weight generation unit is configured to determine a second layover area mask of the ground object in the longitude and latitude coordinate system according to the digital surface model data and the radar domain coordinates, and determine the geocoding coordinates and weights of each layover pixel point corresponding to the second layover area mask; The matching unit is configured to match the strong scattering area mask and the first layover area mask to obtain an overlapping area between the first layover area mask and the strong scattering area mask; The correction amount model construction unit is configured to construct a correction amount model of the target area according to each geocoding coordinate and the weight; The correction processing unit is configured to correct the overlapping area by using the correction amount model to obtain corrected SAR image data.

[0015] Third aspect, the present invention further provides a computer device, which includes a memory, a processor, and a transceiver, which are connected through 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 execute the terrain correction geocoding method described above.

[0016] Fourth aspect, the present invention further provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is run, the L terrain correction geocoding method described above is implemented.

[0017] The present invention provides a terrain correction geocoding method, system, device and medium. Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: (1) According to the high correlation between the strong scattering region and the layover region, correct the deviation of the geocoding coordinates caused by radar parameter errors, and achieve more accurate terrain correction.

[0018] (2) It neither depends on the simulated SAR image algorithm nor on the image matching algorithm. The algorithms used are more mature, concise and efficient, and thus have higher adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a schematic diagram of the steps of a terrain correction geocoding method provided by a preferred embodiment of the present invention; Figure 2 is a pixel value statistical histogram of multi-look amplitude data provided by a preferred embodiment of the present invention; Figure 3 is a visualization result after histogram truncation and normalization provided by a preferred embodiment of the present invention; Figure 4 is a strong scattering region mask provided by a preferred embodiment of the present invention; Figure 5 is a distribution diagram of geocoding coordinate points in the radar coordinate system provided by a preferred embodiment of the present invention; Figure 6 is a statistical histogram of the conversion of the first layover degree matrix provided by a preferred embodiment of the present invention; Figure 7 is a schematic diagram of the first layover region mask provided by a preferred embodiment of the present invention; Figure 8 is a schematic diagram of the second layover region mask provided by a preferred embodiment of the present invention; Figure 9 is the distribution of layover pixel points before (a) and after (b) merging provided by a preferred embodiment of the present invention; Figure 10 is a schematic diagram of the overlapping degree of the strong scattering region mask and the first layover region mask before matching provided by a preferred embodiment of the present invention; Figure 11 is a schematic diagram of the overlapping degree of the strong scattering region mask and the first layover region mask after matching provided by a preferred embodiment of the present invention; Figure 12 is a schematic diagram of the structure of a terrain correction geocoding system provided by a preferred embodiment of the present invention; Figure 13 is a schematic diagram of the structure of a computer device provided in one of the embodiments of the present invention. Detailed implementation manners

[0020] The following specifically elaborates on the implementation manners of the present invention. The provided examples are only for illustrative purposes and should not be construed as a limitation to the present invention. The accompanying drawings are for reference and illustration only and do not constitute a limitation to the protection scope of the present invention. Based on the examples in the present invention, all other examples obtained by those of ordinary skill in the art without creative efforts fall within the protection scope 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 construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise stated, the meaning of "a plurality" is two or more.

[0021] In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", "connected to" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for illustrative purposes 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 thus cannot be construed as a limitation to the present invention. The term "and / or" used herein includes any and all combinations of one or more of the 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.

[0022] In the description of the present invention, it should be noted that unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the technical field to which this technology belongs. 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. 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.

[0023] In an embodiment of the present invention, a terrain correction geocoding method is provided. Please refer to Figure 1 , and the method includes: S1. Obtain the digital terrain model data, radar parameter data of the target area, 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.

[0024] S2. Perform image transformation processing on the multi-look amplitude data to obtain a strong scattering area mask.

[0025] S3. According to the digital terrain model data, the multi-look radar parameter data, and the multi-look amplitude data, obtain the first layover area mask of the ground object target in the radar domain coordinate system in the digital terrain model data and the radar domain coordinates of the ground object target.

[0026] S4. According to the digital terrain model data and the radar domain coordinates, determine the second layover area mask of the ground object target in the longitude and latitude coordinate system, and determine the geocoding coordinates and weights of each layover pixel point corresponding to the second layover area mask.

[0027] S5. Match the strong scattering area mask and the first layover area mask to obtain the overlapping area of the first layover area mask and the strong scattering area mask.

[0028] S6. Construct a correction amount model of the target area according to each geocoding coordinate and the weight.

[0029] S7. Use the correction amount model to correct the overlapping area to obtain corrected SAR image data.

[0030] The terrain correction geocoding method disclosed in the preferred embodiment of the present invention performs terrain correction geocoding based on the high correlation between the layover area and the strong scattering area of the SAR image. Most of the level 1 products of synthetic aperture radar images are single-look complex data. Single-look complex data has the original highest resolution, but it has relatively large speckle noise. To reduce the influence of noise, multi-look processing needs to be performed 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 the speckle noise in the image and improving the signal-to-noise ratio.

[0031] In the 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 implementation methods for 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.

[0032] The time-domain multi-look processing includes mean filtering processing and downsampling processing. Among them, the width of the mean filtering window in the mean filtering processing is equal to the number of looks in the range direction, and the height of the mean filtering window in the mean filtering processing is equal to the number of looks in the azimuth direction.

[0033] The frequency-domain multi-look processing includes two-dimensional Fourier transform, spectrum division, inverse two-dimensional Fourier transform, and mean calculation. Perform two-dimensional Fourier transform on the radar parameter data and image data, then divide the spectrum into several sub-spectra according to the specified number of looks in the range direction and the number of looks in the azimuth direction, and then perform inverse two-dimensional Fourier transform on each sub-spectrum to obtain the time-domain images of several sub-spectra. Perform pixel-by-pixel mean calculation on the time-domain images of the sub-spectra, and finally obtain the multi-look radar parameter data and multi-look amplitude data realized in the frequency domain. After multi-look processing, the multi-look radar parameter data and multi-look amplitude data only contain amplitude information, and the image resolution is reduced, but it can effectively suppress speckle noise.

[0034] Furthermore, perform image transformation processing on the multi-look amplitude data to obtain a strong scattering region mask. Specifically, according to the multi-look amplitude data, generate a pixel value histogram of the multi-look amplitude data. The physical meaning of the multi-look amplitude data is the sum of the backscattered waves given by all ground targets within the radar resolution cell. There are mainly two sources of strong scattering regions: one is from ground targets with a high backscattering coefficient, such as some buildings and corner reflectors, etc.; the other is from steep terrain areas. Due to the side-looking imaging mechanism of the SAR system in this area, there will be foreshortening and layover phenomena, resulting in the superposition of the scattered waves of multiple ground targets within one radar resolution cell, and thus showing a relatively high pixel value in the image. Therefore, the strong scattering region mask actually contains both the region with a high backscattering coefficient and the position information of the layover region in the image generated by the SAR system.

[0035] The pixel values of the multi-look amplitude data have a large dynamic range, such as Figure 2 shown as the pixel value statistical histogram of the multi-look amplitude data. As can be seen from Figure 2 , most of the pixel values of the multi-look amplitude data are concentrated in the range of lower pixel values. To facilitate the visualization of the multi-look amplitude data, truncate and stretch the pixel value statistical histogram of the multi-look amplitude data. Specifically, truncate the pixel values greater than the pre-truncation percentage of the pixel values in the pixel value histogram to 1, and truncate the pixel values less than the post-truncation percentage of the pixel values to 0. The truncation percentage is obtained through presetting. In this application, the truncation percentage is 2.5%, as shown by the dotted line in Figure 2 , that is, truncate the red part in Figure 2 . Normalize the remaining pixel values in the pixel value histogram to the values between 0 and 1, that is, normalize the pixel values in the blue part in Figure 2 to the values between 0 and 1. Convert the truncated and normalized pixel values into an image, as shown in Figure 3 to the values between 0 and 1. Convert the truncated and normalized pixel values into an image, as shown in Figure 3The visualized result after histogram truncation and normalization is shown, and a mask for the strong scattering region is obtained based on the visualized result. In a preferred embodiment of the present invention, pixels greater than the top 2.5% of the pixel values are regarded as pixels in the strong scattering region, and a mask for the strong scattering region can be obtained, as shown in Figure 4 shown.

[0036] Furthermore, based on the digital surface model data, multi-look radar parameter data, and multi-look amplitude data, a first overlapping region mask of the ground object in the digital surface model data in the radar domain coordinate system and the radar domain coordinates of the ground object are obtained. Specifically, first, according to the digital surface model data, multi-look radar parameter data, and multi-look amplitude data, the row and column numbers of any grid data point in the digital surface model data on the multi-look amplitude data are solved by using the backward geocoding algorithm, that is, the azimuth index and the range index.

[0037] Before performing the backward geocoding algorithm operation, the digital surface model data needs to be preprocessed. The preprocessing includes elevation datum conversion, DSM resampling, and conversion of geodetic coordinates to Earth-centered, Earth-fixed coordinates. Since the elevation datums of different digital surface models are different, in order to avoid introducing elevation errors, the elevation datum of the digital surface model data needs to be uniformly converted to the WGS84 ellipsoidal height datum; then the digital surface model data is resampled to the same resolution as the multi-look amplitude data; finally, the geodetic coordinates (longitude, latitude, height) of each grid data point in the digital surface model data are converted to ECEF coordinates (x, y, z) as the input of the backward geocoding algorithm.

[0038] The backward geocoding algorithm needs to construct a strict geometric positioning model of the multi-look amplitude data, that is, the range-Doppler model. The range-Doppler model includes a range equation and a Doppler equation, as shown below: Among them, c is the speed of light value, λ is the radar carrier wavelength, τ rg is the range delay, t az is the azimuth time at the Doppler moment, (X T , Y T , Z T ) are the Earth-centered, Earth-fixed coordinates of the ground object, (X s , Y s , Z s ) are the Earth-centered, Earth-fixed coordinates of the SAR sensor platform at the Doppler moment, (V X , V Y , V Z ) are the velocities of the SAR sensor platform at the Doppler moment, f D is the Doppler frequency.

[0039] The Doppler equation, which is the second equation in the range-Doppler model equation set, contains only one unknown parameter t in the azimuth time az , and the Newton iteration method is used to solve this unary nonlinear equation, so as to solve the azimuth time t corresponding to the ground object target az .

[0040] Substitute the solution result of the azimuth time t az into the range equation, that is, the first equation in the range-Doppler model equation set, and the range delay τ corresponding to the ground object target can be calculated rg .

[0041] Finally, according to the range and azimuth resolutions (Δτ, Δt) and the reference time (τ rg0 , t az0 ), the range delay and azimuth time are converted into the range index I rg and the azimuth index I az , and the conversion formula is: Finally, the range index and azimuth index are used as the radar domain coordinates of the ground object target.

[0042] Furthermore, according to the range index and azimuth index, the first layover area mask of the ground object target in the radar domain coordinate system in the digital surface model data is obtained. The physical meaning of the layover mask is that the scattering echoes of multiple ground object targets are superimposed in one radar resolution unit, and on the geocoded coordinates, it means that one grid in the radar image contains the geocoded coordinate points of multiple ground object targets. As Figure 5 shown in the distribution diagram of geocoded coordinate points in the radar coordinate system, according to Figure 5 , it can be known that the denser the geocoded coordinate points in the radar coordinate system, the higher the layover degree of the area, and it often corresponds to a higher pixel value in the image data obtained by the SAR system. In the present invention, the layover degree is measured by counting the number of geocoded coordinate points falling in the same grid. Specifically, according to the range index and azimuth index, the grid of the radar domain coordinate system where each ground object target is located is determined, and the first number of ground object targets in each grid of the radar domain coordinate system is counted, and according to the digital surface model data and radar domain coordinates, the grid of the longitude and latitude coordinate system where each ground object target is located is determined, and the second number of ground object targets in each grid of the longitude and latitude coordinate system is counted.

[0043] Specifically, denote N rg as the number of grids in the range direction of the multi-look amplitude image, N az as the number of grids in the azimuth direction of the multi-look amplitude image, N lat as the number of grids in the latitude direction after DSM resampling of the digital surface model data, N lonis the number of grids in the longitude direction after resampling the digital terrain model data in DSM. Initialize the layover degree matrix L in the radar coordinate system as L = zero s (N az , N rg ) is a matrix of all zeros, and the layover degree matrix Q in the latitude and longitude coordinate system is Q = -1 * ones(N lat , N lon ) is a constant matrix.

[0044] Traverse the geocoding coordinates of all grid data points (i, j) after resampling the digital terrain model data in DSM When the geocoding coordinate point satisfies and , execute: L[y, x] = L[y, x] + 1 Q[i, j] = x + y * N rg where round represents rounding to the nearest integer operation, x represents the rounded value of the geocoding range coordinate of the grid data point (i, j), y represents the rounded value of the geocoding azimuth coordinate of the grid data point (i, j), L[y, x] represents the element value of the y-th row and x-th column of the matrix L, and Q[i, j] represents the element value of the i-th row and j-th column of the matrix Q.

[0045] Furthermore, traverse each grid data point (i, j) of the layover degree matrix Q in the latitude and longitude coordinate system. If Q[i, j] is equal to -1, then set Q[i, j] to 0; otherwise, execute: y = floor(Q[i, j] / N rg ) x = Q[i, j] - y * N rg Q[i, j] = L[y, x] where floor represents the floor operation.

[0046] Through the above statistical operations, the first layover degree matrix L in the radar domain coordinate system and the second layover degree matrix Q in the latitude and longitude coordinate system are obtained. For the first layover degree matrix, it is used to obtain the first layover area mask of the ground object target in the radar domain coordinate system. Specifically, convert the first layover degree matrix into a statistical histogram, as Figure 6 shown. Determine the binarization threshold. In the preferred embodiment of this application, the binarization threshold is 10%. Mark the pixels with a layover degree greater than the binarization threshold as the layover area, and obtain the first layover area mask of the ground object target in the radar domain coordinate system, as Figure 7 shown as the schematic diagram of the first layover area mask.

[0047] For the second overlay degree matrix Q in the longitude-latitude coordinate system, it is used to determine the second overlay area mask of the ground object target in the longitude-latitude coordinate system, and determine the geocoding coordinates and weights of each overlay pixel corresponding to the second overlay area mask. Specifically, convert the second overlay degree matrix into a statistical histogram, mark the pixels with an overlay degree greater than the second binary threshold as the overlay area, and obtain the second overlay area mask of the ground object target in the radar domain coordinate system, as Figure 8 shown in the schematic diagram of the second overlay area mask. Determine the geocoding coordinates of each grid within the second overlay area mask, and use them as the geocoding coordinates of each overlay pixel corresponding to the second overlay area mask. According to the second overlay degree matrix, calculate the overlay degree value of each overlay pixel, and use the overlay degree value as the weight of the corresponding overlay pixel. The weight calculation formula is as follows: where N represents the total number of overlay pixels, Q i represents the overlay degree value of the i-th overlay pixel, and W i represents the weight of the i-th overlay pixel.

[0048] Furthermore, for areas with a relatively high overlay degree, multiple adjacent overlay pixels often concentrate within a single radar resolution unit. Due to their close proximity, the pixel values of the radar images corresponding to these adjacent overlay pixels are approximately equal. Set a distance threshold and merge the overlay pixels whose mutual distance is less than the distance threshold into an equivalent point. As Figure 9 shown in the distribution of overlay pixels before (a) and after (b) merging. The shade of the color reflects the weight difference of the overlay pixels. The geocoding coordinates of the equivalent point are the average of the coordinates of its adjacent overlay pixels, and the weight is the sum of the weights of its adjacent overlay pixels. After merging, the amount of data to be processed is reduced, thereby improving the algorithm operation efficiency.

[0049] Furthermore, match the strong scattering area mask and the first overlay area mask to obtain the overlapping area of the first overlay area mask and the strong scattering area mask. Since the first overlay area mask and the strong scattering area mask are highly correlated, when the two masks are matched, their correlation is the largest. Denote the strong scattering area mask as M S , and the first overlay area mask as M L . Use the frequency domain method to implement the correlation operation of the two masks. Specifically, determine the search ranges in the range and azimuth directions for rough matching according to prior knowledge; respectively pad zeros to M S and M L so that their sizes meet the search ranges in the range and azimuth directions; then perform a two-dimensional Fourier transform on the strong scattering area mask to obtain the strong scattering area mask spectrum F s, perform a two-dimensional Fourier transform on the first stack of masked area masks to obtain the radar domain stacked mask spectrum F L , which is specifically expressed as follows: F S = fft2(M s ) F L = fft2(M L ) where fft2 represents the two-dimensional Fourier transform.

[0050] Furthermore, based on the strong scattering region mask spectrum and the radar domain stacked mask spectrum, calculate the frequency domain response spectrum. The calculation formula is: where F R represents the frequency domain response spectrum.

[0051] Furthermore, perform an inverse two-dimensional Fourier transform on the frequency domain response spectrum to obtain the time domain response graph, which is specifically expressed as follows: R = ifft2(F R ) where R represents the time domain response graph, and ifft2 represents the inverse two-dimensional Fourier transform.

[0052] Furthermore, find the maximum value in the time domain response graph, and use the time domain response graph corresponding to the maximum value as the overlapping area of the first stack of masked area masks relative to the strong scattering region masks.

[0053] As Figure 10 shown, it is a schematic diagram of the overlapping degree of the strong scattering region mask and the first stack of masked area masks before matching. As Figure 11 shown, it is a schematic diagram of the overlapping degree of the strong scattering region mask and the first stack of masked area masks after matching. Among them, the yellow pixels represent the strong scattering region mask, the blue pixels represent the first stack of masked area masks, and the white pixels represent the overlapping area of the two. It can be observed that there are many regions with similar geometric structures between the strong scattering region mask and the first stack of masked area masks, indicating a high correlation between the stacked mask region and the strong scattering region. After matching by the above method, the overlapping degree of the two masks has increased significantly.

[0054] Further, a correction amount model for the target area is constructed based on each geocoding coordinate and the weight. During the process of the SAR system's earth observation, it will be affected by multiple error sources. Some error sources only affect one of the range dimension or the azimuth dimension. For example, the atmospheric propagation delay only affects the range delay, and the satellite ephemeris error only affects the azimuth time; while some error sources will affect both the range dimension and the azimuth dimension, and overall show spatial variability in the imaging scene. Therefore, in the implementation of the present invention, a polynomial related to the range index and the azimuth index is used to fit the deviation of the geocoding coordinates caused by various error sources. In the preferred embodiment of the present application, the correction amount function relational expression is: f rg (x,y) = a0 + a1x + a2y f az (x,y) = b0 + bx + b2y Wherein, (x, y) represents the geocoding coordinate before correction, f rg (x, y) represents the range correction amount of the geocoding coordinate of the layover pixel point, f az (x, y) represents the azimuth correction amount of the geocoding coordinate of the layover pixel point, a i 、b i (i = 0, 1, 2) represents the polynomial coefficients to be solved.

[0055] Input the geocoding coordinates of each layover pixel point corresponding to the second layover area mask into the correction amount function relational expression to obtain the corrected geocoding coordinates, which are specifically expressed as: x′ = x + f rg (x,y) y' = y + f az (x,y) Wherein, (x′, y′) represents the geocoding coordinate before correction.

[0056] Further, based on the interpolation operation, the corrected geocoding coordinates and the multi-look amplitude data are processed to obtain the pixel value vector γ = [DN i (1 ≤ i ≤ N).

[0057] Further, according to the weight of each layover pixel point corresponding to the second layover area mask, a weight vector w = [W i (1 ≤ i ≤ N) is constructed.

[0058] Since the degree of layover is highly correlated with the intensity of the scattered wave, which can be mathematically expressed as a high inner product value between vector γ and vector w. If a set of polynomial coefficients can be found such that the inner product value between the pixel value vector of the layover pixel points corrected by the correction amount model and its weight vector is maximized, it can be considered that the deviation of the geocoding coordinates caused by errors has been fully corrected at this time. Therefore, the process of fitting the correction amount model can be transformed into the following optimization problem: where, and are the polynomial coefficients of the correction amount model to be optimized.

[0059] The solution of the above optimization function involves multiple interpolation operations, which is not conducive to the calculation of the gradient. Therefore, the present invention uses a metaheuristic optimization algorithm for solution, such as the particle swarm optimization algorithm. Using the metaheuristic optimization algorithm can avoid gradient calculation. At the same time, the metaheuristic optimization algorithm has a stronger global search ability and can avoid falling into local optimal solutions, and finally obtain the correction amount model parameters.

[0060] After obtaining the correction amount model parameters, at least construct a correction amount model according to the correction amount model parameters.

[0061] Finally, use the correction amount model to correct the matched overlapping area to obtain the corrected SAR image data.

[0062] In a preferred embodiment of the present invention, digital terrain model data, radar parameter data of a target area, and image data obtained by a SAR system are acquired; the radar parameter data is subjected to multi-look processing to obtain multi-look radar parameter data, and the image data is subjected to multi-look processing to obtain multi-look amplitude data; the multi-look amplitude data is subjected to image transformation processing to obtain a strong scattering region mask; according to the digital terrain model data, the multi-look radar parameter data, and the multi-look amplitude data, a first superposition mask region mask of a ground object in the digital terrain model data in the radar domain coordinate system and the radar domain coordinates of the ground object are obtained; according to the digital terrain model data and the radar domain coordinates, a second superposition mask region mask of the ground object in the longitude and latitude coordinate system is determined, and the geographic coding coordinates and weights of each superposition pixel point corresponding to the second superposition mask region mask are determined; the strong scattering region mask and the first superposition mask region mask are matched to obtain an overlapping region of the first superposition mask region mask and the strong scattering region mask; a correction amount model of the target area is constructed according to each geographic coding coordinate and weight; the overlapping region is corrected by using the correction amount model to obtain corrected SAR image data. The terrain correction and geographic coding method provided by this application corrects the deviation of the geographic coding coordinates caused by radar parameter errors according to the high correlation between the strong scattering region and the superposition region, realizes more accurate terrain correction, neither depends on the simulated SAR image algorithm nor depends on the image matching algorithm, and the algorithms used are more mature, simple, and efficient, so it has higher adaptability.

[0063] Correspondingly, as Figure 12 shown, based on a terrain correction and geographic coding method, an embodiment of the present invention further provides a terrain correction and geographic coding system for implementing the terrain correction and geographic coding method disclosed in the embodiment of the present invention. The system includes: a data acquisition and processing unit 1, an image transformation processing unit 2, a backward geographic coding and superposition mask generation unit 3, a geographic coding coordinate and weight generation unit 4, a matching unit 5, a correction amount model construction unit 6, and a correction processing unit 7; The data acquisition and processing unit 1 is configured to acquire digital terrain model data, radar parameter data of a target area, and image data obtained 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; The image transformation processing unit 2 is configured to perform image transformation processing on the multi-look amplitude data to obtain a strong scattering region mask; The backward geographic coding and superposition mask generation unit 3 is configured to obtain a first superposition mask region mask of a ground object in the digital terrain model data in the radar domain coordinate system and the radar domain coordinates of the ground object according to the digital terrain model data, the multi-look radar parameter data, and the multi-look amplitude data; The geocoding coordinate and weight generation unit 4 is configured to determine a second layover area mask of the ground object target in the latitude and longitude coordinate system according to the digital surface model data and the radar domain coordinates, and determine the geocoding coordinates and weights of each layover pixel point corresponding to the second layover area mask; The matching unit 5 is configured to match the strong scattering area mask and the first layover area mask to obtain an overlapping area of the first layover area mask and the strong scattering area mask; The correction amount model construction unit 6 is configured to construct a correction amount model of the target area according to each of the geocoding coordinates and the weights; The correction processing unit 7 is configured to correct the overlapping area by using the correction amount model to obtain corrected SAR image data.

[0064] For the specific limitations of a terrain correction geocoding system, reference may be made to the above limitations of a terrain correction geocoding method, which will not be elaborated here. Those of ordinary skill in the art can realize that, in combination with the modules and steps described in the embodiments disclosed in the present invention, they can be implemented in hardware, software, or a combination of both. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0065] As Figure 13 shown, a computer device provided by an embodiment of the present invention includes 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, it implements the steps in the embodiment of the terrain correction geocoding method as described above, such as Figure 1 the steps S1 to S7 described in

[0066] Those skilled in the art can understand that the Figure 13 illustration is only an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the computer device may further include input and output devices, network access devices, buses, etc.

[0067] The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the computer device and connects all parts of the computer device using various interfaces and lines.

[0068] The memory can be used to store the computer programs and / or modules. The processor realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and by invoking the data stored in the memory. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function (such as the sound playback function, the image playback function, etc.); the data storage area can store the data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, memory, plug-in hard disks, Smart Media Cards (SMCs), Secure Digital (SD) cards, Flash Cards, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0069] Among them, if the modules integrated in the computer device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying 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), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0070] Those of ordinary skill in the art can understand that to implement all or part of the processes in the above-mentioned embodiment methods, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. Among them, the storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0071] Correspondingly, an embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. Among them, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the steps in the terrain correction geocoding method in the above-mentioned embodiment, for example Figure 1 the steps S1 to S7 described in

[0072] A 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. Obtain the digital surface model data of the target area, radar parameter data, and image data obtained through 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; perform image transformation processing on the multi-look amplitude data to obtain a strong scattering area mask; according to the digital surface model data, multi-look radar parameter data, and multi-look amplitude data, obtain the first superposition mask area mask of the ground object target in the radar domain coordinate system and the radar domain coordinates of the ground object target in the digital surface model data; according to the digital surface model data and radar domain coordinates, determine the second superposition mask area mask of the ground object target in the longitude and latitude coordinate system, and determine the geocoding coordinates and weights of each superposition pixel point corresponding to the second superposition mask area mask; match the strong scattering area mask and the first superposition mask area mask to obtain the overlapping area of the first superposition mask area mask and the strong scattering area mask; construct a correction amount model of the target area according to each geocoding coordinate and weight; use the correction amount model to correct the overlapping area to obtain the corrected SAR image data. The terrain correction geocoding method provided by this application corrects the deviation of the geocoding coordinates caused by radar parameter errors according to the high correlation between the strong scattering area and the superposition mask area, realizes more accurate terrain correction, neither depends on the simulated SAR image algorithm nor depends on the image matching algorithm, and the algorithms used are more mature, concise and efficient, so it has higher adaptability.

[0073] Each embodiment in this specification is described in a progressive manner. For the parts that are the same or similar in each embodiment, they can be referred to each other. 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 technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not conflict, it should be considered as the scope recorded in this specification.

[0074] The above embodiments only represent several preferred embodiments of the present invention, and the description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention. It should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and replacements can be made, and these improvements and replacements should also be regarded as the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A terrain correction geocoding method, characterized in that: The method comprises: Acquire digital surface model data, radar parameter data and image data acquired by the SAR system of the target area; perform multi-view processing on the radar parameter data to obtain multi-view radar parameter data, and perform multi-view processing on the image data to obtain multi-view amplitude data; Performing image transformation processing on the multi-view amplitude data to obtain a strong scattering area mask; According to 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 target in the digital surface model data in a radar domain coordinate system and the radar domain coordinates of the ground object target are obtained; according to the digital surface model data and the radar domain coordinates, a second overlay region mask of the ground object target in a latitude and longitude coordinate system is determined, and the geocoding coordinates and weight of each overlay pixel point corresponding to the second overlay region mask are determined; the strong scattering region mask and the first overlay region mask are matched to obtain an overlapping region of the first overlay region mask and the strong scattering region mask; Constructing a correction model for the target area according to each of the geocoded coordinates and the weight; The correction amount model is used to correct the overlapping area to obtain corrected SAR image data.

2. The terrain correction geocoding method according to claim 1, characterized in that: 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, characterized in that: The method of obtaining, according to the digital surface model data, the multi-look radar parameter data and the multi-look amplitude data, a first overlapping 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 comprises: Constructing a strict geometric positioning model of the image data according to the digital surface model data, the multi-view radar parameter data and the multi-view amplitude data, wherein the strict geometric positioning model includes a distance equation and a Doppler equation; The Doppler equation is solved by using Newton iteration method to obtain the azimuth time corresponding to the ground object target in the digital surface model data; Substituting the azimuth time into the distance equation to obtain the distance delay corresponding to the ground object; Based on the range resolution, the azimuth resolution and the reference time, converting the azimuth time and the range delay into a range index and an azimuth index; 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, characterized in that: The step of obtaining, according to the digital surface model data, the multi-look radar parameter data and the multi-look amplitude data, a first overlapping 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, further comprises: Determine, according to the range index and the azimuth index, a radar domain coordinate system grid data point where 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 region mask of the ground object target in the radar domain coordinate system.

5. The terrain correction geocoding method according to claim 4, characterized in that: The step of determining a second overlapping area mask of the ground object target in a latitude and longitude coordinate system according to 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, comprises: Determine, according to the digital surface model data and the radar domain coordinates, a latitude and longitude coordinate system grid data point where each of the ground object targets is located, and count the second number of the ground object targets in each of the latitude and longitude coordinate system grid data points; Generate a second overlap degree matrix in the longitude and latitude coordinate system according to the second quantity, perform binarization processing on the second overlap degree matrix, and obtain a second overlap area mask of the ground object target in the longitude and latitude coordinate system; Using the determined geocoded coordinates of each of the grids within the second overlapping region mask as the geocoded coordinates of each overlapping pixel point corresponding to the second overlapping region mask; The overlap degree value of each of the overlapped pixel points is calculated according to the second overlap degree matrix, and the overlap degree value is used as the weight corresponding to the overlapped pixel point.

6. The terrain correction geocoding method according to claim 5, characterized in that: 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 mask 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. The terrain correction geocoding method according to claim 1, characterized in that: The step of constructing the correction amount model of 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 overlapped pixel points; Obtaining correction amount 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 terrain correction geocoding system, implementing the terrain correction geocoding method according to any one of claims 1 to 7, characterized in that: The system comprises: a data acquisition 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, radar parameter data and image data obtained by the SAR system of the target area; perform multi-view processing on the radar parameter data to obtain multi-view radar parameter data, and perform multi-view processing on the image data to obtain multi-view 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 geographic coding and overlay mask generation unit is used 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 according to the digital surface model data, the multi-view radar parameter data and the multi-view amplitude data; The geocoding coordinate and weight generating unit is used to determine the second overlapping area mask of the ground object target in the longitude and latitude coordinate system according to 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 used to match the strong scattering area mask with the first overlapping area mask to obtain an overlapping area between the first overlapping area mask and the strong scattering area mask; The correction amount model building unit is used to build 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.

9. 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 as described in any one of claims 1 to 7.

10. 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 7 is implemented.

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