Method for monitoring coast reclamation condition through satellite remote sensing

Abstract

The invention relates to the technical field of remote sensing monitoring, in particular to a method for monitoring the coastal reclamation condition through satellite remote sensing. The method comprises the following steps: acquiring SAR image data of a pair of target monitoring coast areas, and carrying out speckle noise suppression and distance Doppler terrain correction processing; according to the method, pixels in a main image and an auxiliary image in a deformation area in processed SAR image pair data are obtained, the coherence coefficient of the deformation area is calculated, and the deformation area within a set time range value is judged to be a candidate sea reclamation change suspected area according to the coherence coefficient of the deformation area and a coherence coefficient threshold value of the deformation area. According to the method, the candidate suspected area of the sea reclamation change is determined through the coherence coefficient focused on the deformation area, blind search of the whole monitoring range is avoided, the specific position of the sea reclamation change is accurately positioned, so that the possibility of misjudgment is reduced, meanwhile, the coherence coefficient focused on the deformation area can quickly exclude most deformation areas without change, and the accuracy of the sea reclamation change is improved. And the monitoring efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing monitoring, in particular to a method for monitoring reclamation of a coast by satellite remote sensing. BACKGROUND

[0002] SAR has the imaging ability of all-weather and all-day, and can penetrate clouds and fog, making up for the deficiency of optical remote sensing. However, the accuracy of using SAR images alone for feature classification and boundary extraction is relatively low, especially in a complex coastal area. During the process of obtaining SAR image data of the complex coastal area, the geometric accuracy is affected by satellite orbit data, so that the original orbit data has certain errors, resulting in a deviation between the feature position in the SAR image and the actual geographical position. In addition, the speckle noise is randomly distributed as granular noise on the SAR image, making the texture of the SAR image data fuzzy and complex, increasing the difficulty and error of identification. At the same time, there is geometric distortion in the process of obtaining SAR image data, which further causes the spatial position of the feature in the SAR image to deviate and deform, resulting in a deviation between the reclamation area on the SAR image and the actual geographical position, which is easy to cause misjudgment or omission. Therefore, the speckle noise and geometric distortion of the SAR image bring challenges to the identification of the reclamation change suspicious area. Overall, the existing coast monitoring technology is seriously disturbed by weather conditions such as clouds and rain, and the speckle noise and geometric distortion in the image data reduce the reliability of the identification of the reclamation change suspicious area. At the same time, most of the existing technologies analyze the entire monitoring area, which not only increases the data processing amount and computational complexity, resulting in a decrease in monitoring accuracy, but also cannot accurately locate the change area of reclamation activities, which has the possibility of misjudgment, thereby causing the problem of low monitoring efficiency. Therefore, there is an urgent need for a more efficient and accurate method for monitoring reclamation of a coast by satellite remote sensing. SUMMARY

[0003] The present application aims to provide a method for monitoring reclamation of a coast by satellite remote sensing to solve the problems raised in the background.

[0004] To achieve the above-mentioned purpose, the present application provides a method for monitoring reclamation of a coast by satellite remote sensing, characterized by comprising the following method steps: S1, obtaining image pair data of different viewing angles and continuous time sequences of a target monitoring coastal area and processing the same; S2, acquire the processed image pair data, and perform conjugate multiplication as the main image and the auxiliary image to generate an interferogram, form a deformation map and judge a deformation region, wherein: the image data with high pixels is taken as the main image data, and the image data with low pixels is taken as the auxiliary image data; based on the window size of the deformation region, the pixels and the number of pixels of the main image and the auxiliary image are extracted, the coherence coefficient of the deformation region is calculated to judge the candidate reclamation change suspicious region within the set time range value; the main image and the auxiliary image pixels of the candidate reclamation change suspicious region are extracted to capture the change of the ground surface texture, and the filling material of the candidate reclamation change suspicious region is identified; S3, acquire the corresponding region of the candidate reclamation change suspicious region in the interferogram and intercept, perform a morphological opening operation on the intercepted candidate suspicious region of the interferogram, identify the reclamation change suspicious region, and reclamation the reclamation change suspicious region according to the type of filling material.

[0005] As a further improvement of the technical solution, S2.1 in S2, generating an interferogram to remove the phase difference after the terrain phase includes the following method steps, and the following method steps are realized: The processed SAR image pair data is taken as the main image and the auxiliary image respectively, the pixels of the auxiliary image are aligned with the main image, the pixel-level matching is ensured, so as to obtain the two image data after matching and perform conjugate multiplication to generate an interferogram, record the phase of the processed SAR image pair data to calculate the phase difference of the interferogram, obtain the radar wavelength, the vertical baseline, the slant range, the incidence angle and the terrain height through the processed SAR image pair data, calculate the terrain phase, and calculate the phase difference after removing the terrain phase by using the phase difference of the interferogram and the terrain phase.

[0006] As a further improvement of the technical solution, S2.1 in S2, forming a deformation map through the ground deformation variable, and automatically discriminating the deformation region through the deformation map, so as to realize the following method steps for determining that the spatial range of the reclamation activity has changed, and the following method steps are realized: The terrain phase is estimated through the processed SAR image pair data, the phase difference after removing the terrain phase is subtracted from the atmospheric phase, the difference phase is obtained and converted into a ground deformation variable, the ground deformation variable is stored as a matrix with the same size as the processed SAR image pair data, a deformation variable matrix is formed and visualized as an image, and then a deformation map is formed, whether there is a deformation region in the deformation map is judged by using the ground deformation variable; when the ground deformation variable is greater than 0, the region in the deformation map has obvious ground uplift, it is determined that there is a deformation region in the deformation map; when the ground deformation variable is less than 0, the region in the deformation map has obvious ground subsidence, it is determined that there is a deformation region in the deformation map, and if there is a deformation region in the deformation map, it is determined that the spatial range of the reclamation activity has changed.

[0007] As a further improvement of the technical solution, S2.2 in S2 implements the following method steps to automatically identify the candidate reclamation change suspected area: By obtaining the pixels in the main image and the secondary image in the deformation area in the SAR image pair data, and extracting the number of pixels from the window size in the deformation area, the coherence coefficient of the deformation area is calculated based on the pixels in the main image and the secondary image in the deformation area and the number of pixels. The coherence coefficient threshold of the deformation area is set, and the coherence coefficient of the deformation area is used to judge whether the deformation area is a candidate reclamation change suspected area within the set time range value. The number of times when the coherence coefficient of the deformation area is less than the coherence coefficient threshold of the deformation area, and the number of times when the coherence coefficient of the deformation area is greater than the coherence coefficient threshold of the deformation area are recorded. When the number of times is less than the number of times and the number of times is greater than the number of times, it is determined that the deformation area exists within the set time range value. The deformation area is a candidate reclamation change suspected area.

[0008] As a further improvement of the technical solution, S2.2 in S2 identifies the following method steps for the uneven fill distribution range and fill material of the candidate reclamation change suspected area, and performs the following method steps: The processed SAR image pair data corresponding to the candidate reclamation change suspected area and the preprocessed optical image data are obtained and fused. The change of the ground texture is captured by analyzing the fused image data. The uneven fill distribution range and fill material of the candidate reclamation change suspected area are identified according to the change of the ground texture.

[0009] As a further improvement of the technical solution, S3 includes the following method steps for identifying the reclamation change suspected area: The area of the candidate reclamation change suspected area is calculated according to the uneven fill distribution range of the candidate reclamation change suspected area. By calculating the area, the actual coverage area of reclamation can be accurately defined. The corresponding area in the interferogram is found through the candidate reclamation change suspected area, and the candidate suspected area of the interferogram is intercepted. By directly positioning the corresponding area in the interferogram through the candidate reclamation change suspected area and intercepting, large-scale processing of the entire interferogram is avoided, and the amount of data processing is reduced. The candidate suspected area of the interferogram is converted into a binary image. After the candidate suspected area is converted into a binary image, the data structure is simpler, which facilitates subsequent morphological operations and connected region extraction, and improves the processing speed and efficiency. By performing the erosion operation first and then the dilation operation on the binary image of the candidate suspected area, a connected region is formed and the connected region greater than the area threshold is retained. The connected region is the reclamation change suspected area. According to the fill material type and the area of the candidate reclamation change suspected area, the reclamation change suspected area is filled in a targeted manner, which can reasonably plan the development and utilization of reclamation resources, determine the areas that need to be focused on and managed, and avoid waste of a large amount of reclamation resources.

[0010] Compared with the prior art, the present application has the following advantages: 1. The method for monitoring the filling situation of the coast by satellite remote sensing, by obtaining and processing the pixels in the main image and the auxiliary image in the deformation region in the SAR image pair data, extracting the number of pixels from the window size in the deformation region, calculating the coherence coefficient of the deformation region by the pixels in the main image and the auxiliary image in the deformation region and the number of pixels, and using the coherence coefficient of the deformation region and the coherence coefficient threshold of the deformation region to determine the candidate filling change suspicious region in the set time range, the candidate filling change suspicious region is determined by focusing on the coherence coefficient of the deformation region, which avoids blind search of the entire monitoring range, thereby reducing the possibility of misjudgment, and focusing on the coherence coefficient of the deformation region can quickly exclude most of the deformation regions without change, thereby improving the monitoring efficiency.

[0011] 2. The method for monitoring the filling situation of the coast by satellite remote sensing, by estimating the atmospheric phase from the processed SAR image pair data, subtracting the atmospheric phase from the phase difference after removing the terrain phase, obtaining the differential phase and converting it into a ground deformation variable, storing the ground deformation variable as a matrix with the same size as the processed SAR image pair data, forming a deformation variable matrix and visualizing it as an image, and then forming a deformation map, using the ground deformation variable to determine the existence of a deformation region in the deformation map, and then determining that the spatial range of the filling activity has changed, the deformation region of the deformation map can avoid redundant calculation of irrelevant regions, greatly reduce the amount of data to be processed, and accurately identify the spatial range change of the filling activity, thereby improving the monitoring accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0012] Fig. 1 is the overall step block diagram of the present application; Fig. 2 is the flowchart of the present application for determining the spatial range change of the filling activity using the deformation map; Fig. 3 is the flowchart of the present application for identifying the candidate filling change suspicious region for the deformation region. DETAILED DESCRIPTION

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0014] Embodiment 1 The present application provides a method for monitoring the filling situation of the coast by satellite remote sensing, please refer toFigs. 1-3 , comprising the following method steps: S1, acquiring image pair data of different view angles of the target monitoring coastal area in continuous time series and processing the same; S1 comprises the following method steps: S1.1, first, a target monitoring coastal area is selected, and then SAR (Synthetic Aperture Radar) image data of different view angles of the target monitoring coastal area in continuous time series is acquired through satellite remote sensing technology, so as to extract SAR image pair data (referring to combining SAR image data of two different view angles in continuous time series to form SAR image pair data), thereby ensuring high-frequency monitoring capability, and at the same time, high-resolution optical image data matching the time window of the SAR image data is acquired through satellite remote sensing technology, and then the SAR image pair data is processed for precise orbit data updating, so as to obtain SAR image pair data with updated precise orbit, and then the SAR image pair data with updated precise orbit is processed for radiometric calibration (converting DN value into backscattering coefficient value), so as to obtain radiometrically calibrated SAR image pair data, and then the radiometrically calibrated SAR image pair data is processed for speckle noise suppression by Lee filtering, so as to obtain SAR image pair data with processed noise, thereby reducing texture blur and complexity of the SAR image pair data, and thus reducing recognition difficulty and error; The speckle noise suppression processing by Lee filtering comprises the following method steps: Step 1, collect the radiometrically calibrated SAR image pair data, and then check the basic information of the radiometrically calibrated SAR image pair data, such as the size (number of rows and columns) of the SAR image pair, the number of bands, and whether there are missing values or abnormal values in the data type, and if so, pre-process the SAR image pair data by using appropriate methods (such as interpolation method, statistical method), so as to obtain pre-processed SAR image pair data; Step 2, extract the resolution and noise level of the SAR image pair from the pre-processed SAR image pair data to preliminarily select the window size (such as 3x3, 5x5, 7x7), and then select the optimal window size by observing the filtering effect through experiments of different window sizes (the window size will affect the filtering effect, a larger window can effectively suppress noise, but will cause blur of the edges and details of the SAR image pair; a smaller window is better for preserving details, but the noise suppression capability is relatively weak); Step 3, traverse each pixel in the pre-processed SAR image pair data Calculate the average value of all pixel values within the optimal window size centered on the pixel And the variance Then obtain the equivalent number of views (ENL) through statistical analysis of the uniform area in the pre-processed SAR image pair data According to the average value of the pixel value Estimate the noise variance by using the equivalent number of views (ENL) method ; Step 4: Variance of pixel values and noise variance Calculate filter coefficients ,when When ;when When ; By Pixel , filter coefficient and the average of the pixel values Calculate the filtered pixel value , according to the filtered pixel values, they are combined according to the arrangement mode in the pre-processed SAR image pair data to form noise-processed SAR image pair data; S1.2. Check the geographical range of the high-resolution optical image data to ensure that the geographical range of the high-resolution optical image data is consistent with the geographical range of the noise-processed SAR image pair data, obtain the checked optical image data, and then preprocess the checked optical image data (such as resampling, interpolation or cropping) to make it consistent with the resolution and coverage of the noise-processed SAR image pair data, thereby obtaining the preprocessed optical image data and obtaining the imaging parameters in the noise-processed SAR image pair data. The imaging parameters of the SAR image pair include slant range, azimuth, incidence angle, and orbit information. Then, according to the range Doppler model, establish Establish a mapping relationship between the slant range coordinate of each pixel in the noise-processed SAR image pair and the geographic coordinates in the preprocessed optical image data, calculate the terrain height of each pixel using the preprocessed optical image data, and determine the location of the target monitoring coastal area in the noise-processed SAR image pair in combination with the imaging parameters of the SAR image pair. Convert each pixel in the noise-processed SAR image pair from the slant range coordinate system to a geographic coordinate system (such as UTM or longitude and latitude) to obtain the converted SAR image pair data. Then check the converted SAR image pair data to ensure that the location and shape of the target monitoring coastal area conform to the characteristics of the geographic coordinate system. The resolution in the pre-processed optical image data is extracted, resampling is performed, resampled optical image data is obtained for inspection, the geometric precision and image quality in the resampled optical image are ensured to meet the set geometric precision threshold and image quality threshold, the imaging parameters in the pre-processed optical image data are extracted, the shadow area in the image is detected according to the imaging parameters of the optical image and the imaging parameters of the SAR image pair, the occlusion area in the image is identified, the shadow area is filled using an interpolation method (such as linear interpolation or Kriging interpolation), the occlusion area is processed using interpolation or extrapolation, the target monitoring coastal area information covered by the occlusion area is recovered, thereby eliminating or compensating for the influence of the shadow and the occlusion area caused by the terrain undulation, reducing the geometric distortion influence of the terrain undulation on the SAR image, and finally obtaining the processed SAR image pair data; S2, obtaining the processed image pair data, and performing conjugate multiplication as the main image and the auxiliary image to generate an interferogram, and forming a deformation map and judging a deformation area, wherein: the high-pixel image data is taken as the main image data, and the low-pixel image data is taken as the auxiliary image data; based on the window size of the deformation area, the pixels and the number of pixels of the main image and the auxiliary image are extracted, the coherence coefficient of the deformation area is calculated to judge the candidate reclamation change suspicious area within the set time range value; the main image and the auxiliary image pixels of the candidate reclamation change suspicious area are extracted to capture the change of the ground texture, and the reclamation material of the candidate reclamation change suspicious area is identified; S2 includes the following method steps: S2.1, taking the processed SAR image pair data as the main image and the auxiliary image respectively, wherein the high-pixel image data is taken as the main image data, and the low-pixel image data is taken as the auxiliary image data, then the auxiliary image is precisely aligned with the main image to ensure pixel-level matching (error less than 0.1 pixel), thereby obtaining two registered image data and performing conjugate multiplication to generate an interferogram (containing phase and amplitude information), and recording the phase of the processed SAR image pair data and to calculate the phase difference of the interferogram ; The radar wavelength , the vertical baseline (the vertical distance between the satellite orbits when acquiring the two SAR images), the slant range (the distance from the SAR sensor to the ground target), the incidence angle (the angle between the SAR beam and the ground normal), and the terrain height (the height of the ground target relative to the reference ellipsoid) are obtained from the processed SAR image pair data, and then the terrain phase is calculated, and the phase difference of the interferogram is subtracted from the terrain phase , to obtain the phase difference after removing the terrain phase ; Estimating atmospheric phase from SAR image pair data , subtracting the atmospheric phase from the phase difference after removing the terrain phase , to obtain the differential phase , converting the differential phase into the surface deformation variable , wherein the unit of the deformation variable is meter, refers to the radar wavelength, and the unit is meter, is a mathematical constant, storing the surface deformation variable as a matrix with the same size as the processed SAR image pair data, and the value of each pixel point represents the surface deformation variable of the target monitoring coastal area, thereby forming a deformation variable matrix, visualizing the deformation variable matrix as an image, and further forming a deformation map, using the surface deformation variable to determine whether there is a deformation area in the deformation map, when the surface deformation variable is greater than 0, it is determined that there is obvious surface uplift in the area in the deformation map, and it is determined that there is a deformation area in the deformation map, when the surface deformation variable is less than 0, it is determined that there is obvious surface subsidence in the area in the deformation map, and it is determined that there is a deformation area in the deformation map, and if there is a deformation area in the deformation map, it is determined that the spatial range of the reclamation activity has changed.

[0015] S2.2, when the spatial range of the reclamation activity has changed, obtaining the i-th pixel in the primary image and the secondary image in the deformation area from the processed SAR image pair data and , and extracting the number of pixels from the optimal window in the deformation area and , calculating the coherence coefficient of the deformation area from the i-th pixel in the primary image and the secondary image in the deformation area , wherein * refers to complex conjugate, and further setting the time range value and the coherence coefficient threshold of the deformation area, using the coherence coefficient of the deformation area and the coherence coefficient threshold of the deformation area to determine whether the deformation area is a candidate reclamation change suspicious area within the set time range value, recording the number of times when the coherence coefficient of the deformation area is less than the coherence coefficient threshold of the deformation area and the number of times when the coherence coefficient of the deformation area is greater than the coherence coefficient threshold of the deformation area, when the less number of times is higher than the greater number of times, it is determined that there is a sharp decline in the deformation area within the set time range value, and the deformation area is a candidate reclamation change suspicious area, otherwise, it is not a candidate reclamation change suspicious area; The main image and the secondary image pixels of the processed SAR image pair data in the corresponding candidate reclamation change suspicious area, the pixels in the pre-processed optical image data are acquired, and targeted analysis is performed to capture the change of the ground texture (change of texture structure, change of texture roughness, change of texture direction), the uneven filling distribution range and the filling material of the candidate reclamation change suspicious area are identified according to the change of the ground texture, the main image and the secondary image pixels of the processed SAR image pair data and the pixels in the pre-processed optical image data are fused to obtain a fused image, the penetration of the SAR image and the hyperspectral information of the optical image are comprehensively utilized, an image segmentation algorithm such as a segmentation method based on region growing is adopted to segment the fused image, and the filling area is identified according to the segmentation result (filling area contour, filling area internal feature) and combined with the texture change information, and the uneven filling distribution range of the candidate reclamation change suspicious area is determined. The spectral curve of the filling area is extracted from the hyperspectral data in the fused image, different filling materials have different spectral reflection characteristics, for example, the reflectivity of sandstone material is relatively high in the near-infrared band, and the reflectivity of clay material is relatively low in the visible light band, the extracted spectral curve is compared with the spectral library of known filling materials, and the filling material type is determined through a spectral matching algorithm (such as spectral angle mapping, minimum distance classification).

[0016] S3, acquiring the corresponding area of the candidate reclamation change suspicious area in the interference graph and intercepting, performing a morphological opening operation on the intercepted candidate suspicious area of the interference graph to identify the reclamation change suspicious area, and performing targeted reclamation on the reclamation change suspicious area according to the filling material type. S3 includes the following method steps: S3, according to the uneven filling distribution range of the candidate reclamation change suspicious area, the boundary of the filling distribution range is drawn using the polygon tool (GIS software), the drawn polygon is recorded, and the area of the candidate reclamation change suspicious area is directly calculated using the attribute table, the corresponding area in the interferogram is found according to the candidate reclamation change suspicious area, the candidate suspicious area of the interferogram is intercepted, the candidate suspicious area of the interferogram is converted into a binary image, the binary image of the candidate suspicious area is extracted, morphological opening operation is performed on the binary image of the candidate suspicious area, first erosion and then inflation, erosion operation: remove the boundary pixels of the binary image of the candidate suspicious area, reduce the area of the binary image of the candidate suspicious area, and eliminate small spots of the binary image of the candidate suspicious area; inflation operation: restore the boundary of the eroded area in the binary image of the candidate suspicious area, thereby forming a connected region, then an area threshold is set, the area of each connected region is calculated, the connected regions with an area less than the area threshold are removed, only the connected regions with an area greater than the area threshold are retained, the larger connected regions are retained, and finally the output connected region is the reclamation change suspicious area, then the reclamation change suspicious area is filled according to the type of filling material and the area of the candidate reclamation change suspicious area.

[0017] The basic principle, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring coastal reclamation using satellite remote sensing, comprising: S1, obtaining and processing continuous time series image data of different viewing angles of a target coastal area; and Also includes the following method steps: S2. Obtaining processed image pair data and performing conjugate multiplication on the data as a primary image and a secondary image to generate an interference pattern, and simultaneously forming a deformation map and determining the deformation area, wherein: the high-pixel image data is used as the primary image data, and the low-pixel image data is used as the secondary image data; Extract pixels and pixel counts from the primary and secondary images based on the window size of the deformation area, and calculate the coherence coefficient of the deformation area to determine the candidate suspected reclamation change area within the set time range; Extract pixels from the primary and secondary images of the candidate suspected reclamation change areas to capture the changes in surface texture and identify the fill materials in the candidate suspected reclamation change areas; S3. Obtain and intercept the corresponding area of ​​the candidate suspected reclamation change area in the interference map, perform morphological opening operation on the candidate suspected area of ​​the intercepted interference map, identify the suspected reclamation change area, and carry out targeted reclamation on the suspected reclamation change area according to the type of fill material.

2. The method for monitoring coastal reclamation using satellite remote sensing according to claim 1, characterized in that: The step S1 of performing speckle noise suppression on SAR image data and eliminating the geometric distortion of SAR images caused by shadows and overlapping areas caused by terrain undulations includes the following method steps and implements the following steps: S1.1 in the above S1, obtains a pair of SAR image data of continuous time series of different viewing angles of a target monitoring coastal area through satellite remote sensing technology, as well as high-resolution optical image data matching the time window of the SAR image data, thereby extracting SAR image pair data and performing precise orbit data update and radiation calibration processing, and using Lee filtering to suppress speckle noise on the radiation calibrated SAR image pair data.

3. The method for monitoring coastal reclamation using satellite remote sensing according to claim 2, characterized in that: S1.1 in S1 performs speckle noise suppression processing using Lee filtering, including the following method steps: Step ①, collect radiometrically calibrated SAR image pair data, and then check whether the basic information of the radiometrically calibrated SAR image pair data contains missing values ​​or abnormal values. If so, preprocess the data using interpolation and statistical methods; Step ②, extract the resolution and noise level of the SAR image pair through the preprocessed SAR image pair data, and preliminarily select the window size; Step 3: traverse the pixels in the pre-processed SAR image data, calculate the mean and variance of the pixel values ​​within the window size according to the pixels, and then use the equivalent visual number method to estimate the noise variance based on the mean value of the pixel values; Step 4: Variance of pixel values and noise variance Calculate filter coefficients , and then calculate the filtered pixel values, and combine them according to the arrangement method in the pre-processed SAR image pair data to form the noise-processed SAR image pair data.

4. The method for monitoring coastal reclamation using satellite remote sensing according to claim 2, characterized in that: S1.2 in S1 is to perform resampling and interpolation preprocessing on high-resolution optical image data, and at the same time, obtain imaging parameters in the noise-processed SAR image pair data, where the imaging parameters of the SAR image pair include slant range, azimuth, angle of incidence, and orbit information.

5. The method for monitoring coastal reclamation using satellite remote sensing according to claim 4, characterized in that: S1.2 in S1 is to eliminate the geometric distortion of the SAR image caused by the shadow and overlapping areas caused by the terrain undulations, and to implement the following steps: Extract the resolution of the pre-processed optical image data and then resample it to check to ensure that the geometric accuracy and image quality of the resampled optical image meet the set geometric accuracy threshold and image quality threshold; Extract imaging parameters from pre-processed optical image data, detect shadow areas in images based on the imaging parameters of the optical image and the imaging parameters of the SAR image pair, and identify overlapping areas in images; The interpolation method is used to fill the shadow area, and then the extrapolation method is used to process the overlapping area to restore the target monitoring coastal area information covered by the overlapping area, thereby eliminating the influence of shadows and overlapping areas caused by terrain undulations and obtaining processed SAR image data.

6. The method for monitoring coastal reclamation using satellite remote sensing according to claim 5, characterized in that: The method step of S2, which generates an interference pattern to identify the candidate suspected land reclamation change area and the uneven fill distribution range and fill material of the candidate suspected land reclamation change area, includes the following method steps: S2.1 in S2, generating an interference pattern to achieve the phase difference after removing the terrain phase, includes the following method steps and implements the following method steps: The processed SAR image pair data is used as the main image and the secondary image respectively. The pixels of the secondary image and the main image are aligned to ensure pixel-level matching, thereby obtaining the two matched image data and performing conjugate multiplication to generate an interferogram. The phase of the processed SAR image pair data is recorded to calculate the phase difference of the interferogram. The radar wavelength, vertical baseline, slant range, incident angle, and terrain height are obtained from the processed SAR image data, and the terrain phase is calculated; The phase difference after removing the topographic phase is calculated using the phase difference of the interferogram and the topographic phase.

7. The method for monitoring coastal reclamation using satellite remote sensing according to claim 6, characterized in that: S2.1 in S2, forming a deformation map using the surface deformation variable, and then automatically identifying the deformation area using the deformation map, thereby implementing the following method steps for determining whether the spatial scope of the land reclamation activity has changed, and implementing the following method steps: The atmospheric phase is estimated from the processed SAR image data, and the atmospheric phase is subtracted from the phase difference after removing the terrain phase to obtain the differential phase and convert it into the surface shape variable; The surface deformation variables are stored as a matrix with the same size as the processed SAR image data to form a deformation variable matrix and visualized as an image to form a deformation map; Use the surface deformation variables to determine whether there is a deformation area in the deformation map; When the surface deformation variable is greater than 0, there is obvious surface uplift in the area of ​​the deformation map, and it is determined that there is a deformation area in the deformation map; When the surface deformation variable is less than 0, there is obvious surface subsidence in the area of ​​the deformation map, and it is determined that there is a deformation area in the deformation map; If there is a deformation area in the deformation map, it is determined that the spatial scope of the reclamation activity has changed.

8. The method for monitoring coastal reclamation using satellite remote sensing according to claim 7, characterized in that: S2.2 in said S2, implements the following method steps for automatically identifying the candidate land reclamation change suspected area, and implements the following method steps: By obtaining the pixels in the primary and secondary images in the deformed area of ​​the processed SAR image pair data, and then extracting the number of pixels from the window size in the deformed area, the coherence coefficient of the deformed area is calculated by the pixels and the number of pixels in the primary and secondary images in the deformed area; Setting a time range value and a coherence coefficient threshold of the deformation region, and using the coherence coefficient of the deformation region and the coherence coefficient threshold of the deformation region to determine whether the deformation region within the set time range value is a candidate suspected reclamation change region; Record the number of times the coherence coefficient of the deformation area is less than the coherence coefficient threshold of the deformation area, and the number of times the coherence coefficient of the deformation area is greater than the coherence coefficient threshold of the deformation area. When the number of times less than is higher than the number of times greater than, it is determined that there is a sharp drop in the deformation area within the set time range value, and the deformation area is identified as a candidate suspected reclamation change area.

9. The method for monitoring coastal reclamation using satellite remote sensing according to claim 8, characterized in that: S2.2 in said S2, identifying the uneven fill distribution range and fill material of the candidate reclamation change suspected area, and executing the following method steps: Obtaining the primary and secondary image pixels of the processed SAR image pair data corresponding to the candidate suspected reclamation change area and the pixels in the pre-processed optical image data and fusing them; Capture changes in surface texture by conducting targeted analysis on the fused image data; The uneven fill distribution range and fill material of the candidate reclamation change suspicion area are identified based on the changes in surface texture.

10. The method for monitoring coastal reclamation using satellite remote sensing according to claim 9, characterized in that: The S3 includes the following method steps for identifying suspected areas of land reclamation changes: Calculate the area of ​​the candidate suspected land reclamation change region based on the uneven fill distribution range of the candidate suspected land reclamation change region; Find the corresponding area in the interference map through the candidate reclamation change suspected area, intercept the candidate suspected area of ​​the interference map, convert the candidate suspected area of ​​the interference map into a binary map, and extract the binary map of the candidate suspected area; By performing an erosion and then dilation operation on the candidate suspect binary image, a connected area is formed and the connected area larger than the area threshold is retained. The connected area is the suspected area of ​​land reclamation change; Targeted filling is carried out in the suspected reclamation change areas based on the fill material type and the area of ​​the candidate suspected reclamation change areas.

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