Water area extraction method fusing radar backscattering and interference coherence coefficients

Abstract

The invention discloses a water area extraction method fusing radar backscattering and interference coherence coefficients, and belongs to the technical field of remote sensing water area extraction, and the method comprises the following steps: S1, obtaining a polarimetric SAR image, and carrying out the preprocessing of the polarimetric SAR image, and obtaining a backscattering coefficient image and an interference coherence coefficient image; s2, calculating an SDWI water body index according to the backscattering coefficient image; s3, according to the SDWI water body index, determining a water body preliminary extraction result; s4, based on the interference coherence coefficient image, performing fusion processing on the water body preliminary extraction result to generate a fused water body extraction result; and S5, refining the fused water body extraction result to obtain a final water body range. The method not only can accurately calculate the segmentation threshold value, but also can prevent submerged plants and silt from influencing the extraction result, and improves the accuracy of water body area extraction.

Description

Technical Field

[0001] The invention belongs to the technical field of remote sensing water area extraction, and particularly relates to a water area extraction method that integrates radar backscattering and interference coherence coefficient. Background Art

[0002] Lakes, as a vital component of water resources, play a crucial role in the natural water cycle. Lakes not only participate in climate regulation and water conservation, but are also crucial for maintaining ecosystem balance. Lake area is a key indicator of water levels, and accurately extracting and monitoring lake area has significant scientific significance and application value. With the development of Earth observation technology, remote sensing has been widely used for water area extraction and has gradually become a key technical means for lake area extraction. Compared to optical remote sensing, the Sentinel-1 SAR satellite offers advantages over optical remote sensing, including all-weather and all-day coverage, cloud penetration, and rapid data acquisition, providing opportunities for high-precision water body identification and area extraction. Among these, threshold segmentation, a primary method for extracting water area from remote sensing data, primarily involves manual and automatic threshold selection. Manual threshold selection is highly subjective and cannot accurately delineate between water and non-water bodies. Furthermore, this method is time-consuming and unsuitable for large-scale applications with massive amounts of data. For water and non-water body delineation, the OTSU algorithm is a commonly used automatic threshold selection method, which is simple and fast to use. However, automatic threshold selection methods for identifying water and non-water bodies based on SAR imagery are susceptible to factors such as mountain shadows and swamps caused by SAR side-view imaging, leading to misclassification. Furthermore, current water extraction methods based on Sentinel-1 SAR data mostly use single-polarization information. When extracting water area, the reflectance of water bodies and that of submerged plants and sediment in the backscatter coefficient image appear highly similar, due to the influence of submerged plants and sediment. This reduces the accuracy of water area extraction. Summary of the Invention

[0003] In order to solve the above problems, the present invention proposes a water area extraction method that integrates radar backscatter and interference coherence coefficient. The beneficial effect of the above further solution is: in the present invention,

[0004] The technical solution of the present invention is: a water area extraction method integrating radar backscatter and interference coherence coefficient comprises the following steps:

[0005] S1. Acquire polarimetric SAR images and preprocess the polarimetric SAR images to obtain backscattering coefficient images and interferometric coherence coefficient images;

[0006] S2. Calculate the SDWI water index based on the backscatter coefficient images of different polarization modes;

[0007] S3. According to the SDWI water body index, the iterative threshold segmentation method is used to determine the preliminary extraction results of the water body;

[0008] S4. Based on the interference coherence coefficient image, the preliminary water body extraction results are fused to generate a fused water body extraction result;

[0009] S5. Refine the fused water body extraction results to obtain the final water body range.

[0010] Furthermore, in S1, the polarimetric SAR image is preprocessed by orbit correction, radiometric correction, TOPS SAR image de-skewing, cropping, multi-looking, DN value conversion to dB, and geocoding to obtain the backscatter coefficient image;

[0011] In S1, the polarimetric SAR image is preprocessed through orbit correction, radiation correction, TOPS SAR image segmentation, reverse geocoding, TOPS SAR image de-skewing, TOPS SAR image stitching, multi-viewing, interferometry, cropping and geocoding to obtain the interferometric coherence coefficient image.

[0012] Furthermore, in S2, the SDWI water index K SDWI The calculation formula is:

[0013] K SDWI =ln(10*VV*VH);

[0014] Where VV represents the backscatter coefficient of vertical polarization mode, and VH represents the backscatter coefficient of cross-polarization mode.

[0015] Furthermore, S3 includes the following sub-steps:

[0016] S31, dividing the grayscale image corresponding to the SAR image into several gray levels, and calculating the probability of each gray level;

[0017] S32. Calculate the background class probability and the foreground class probability according to the probability of each gray level;

[0018] S33, calculating the average grayscale value of the background class and the average grayscale value of the foreground class according to the background class probability and the foreground class probability;

[0019] S34, calculating the variance between different classes based on the background class probability, the foreground class probability, the average grayscale value of the background class, and the average grayscale value of the foreground class;

[0020] S35, calculating the variance between different elements in the background class and the variance between different elements in the foreground class based on the background class probability, the foreground class probability, the average grayscale value of the background class, and the average grayscale value of the foreground class;

[0021] S36. Calculate the ratio of the sum of the between-class variance and the within-class variance based on the variances between different classes, the variances between different elements in the background class, and the variances between different elements in the foreground class.

[0022] S37, repeat S32-S36 to obtain the ratio of the sum of the inter-class variance and the intra-class variance of each grayscale value, and extract the grayscale value corresponding to the maximum ratio;

[0023] S38, calculating the sum of the entropies of each gray value, and extracting the gray value corresponding to the maximum entropy sum;

[0024] S39, calculating a partition threshold according to the grayscale value corresponding to the maximum ratio and the grayscale value corresponding to the sum of the maximum entropy;

[0025] S310: Determine preliminary water body extraction results based on the division threshold and the SDWI water body index.

[0026] Furthermore, in S31, the probability P of the i-th gray level i The calculation formula is:

[0027]

[0028] Where n i represents the number of pixels with gray value i in the image, and N represents the total number of pixels in the grayscale image;

[0029] In S32, the background class probability P a The calculation formula for (k) is:

[0030]

[0031] Where k represents the segmentation threshold of the grayscale image;

[0032] In S32, the foreground class probability P b (k) calculation formula;

[0033]

[0034] Where L represents the number of gray levels of the grayscale image;

[0035] In S33, the average gray value m of the background class a The calculation formula for (k) is:

[0036]

[0037] In S33, the average gray value m of the foreground class b The calculation formula for (k) is:

[0038]

[0039] In S34, the variance between different classes σ0(k) 2 The calculation formula is:

[0040] σ0(k) 2 =P a (k)P b (k)(m a (k)-m b (k)) 2 ;

[0041] In S35, the variance σ between different elements in the background class a The calculation formula is:

[0042]

[0043] In S35, the variance σ between different elements in the foreground class b The calculation formula is:

[0044]

[0045] In S36, the calculation formula for the ratio θ of the sum of the between-class variance and the within-class variance is:

[0046]

[0047] In S38, the calculation formula for the sum of grayscale entropy λ is:

[0048]

[0049] Where, ln(·) represents the logarithmic function;

[0050] In S39, the calculation formula of the division threshold T1 is:

[0051]

[0052] Where, t a Indicates the grayscale value corresponding to the maximum ratio, t b Represents the grayscale value corresponding to the sum of the maximum entropy.

[0053] Furthermore, S310 includes the following sub-steps:

[0054] S3101, dividing into background area and foreground area using a division threshold;

[0055] S3102, based on the SDWI water index, weighted average the background and foreground areas to obtain a weighted average grayscale value of the background area and a weighted average grayscale value of the foreground area;

[0056] S3103: Pixels with a grayscale value greater than the weighted average grayscale value of the background area and less than the weighted average grayscale value of the foreground area are considered as uncertain areas;

[0057] S3104, iteratively calculating the partition threshold of the uncertain region until the difference between the final partition threshold obtained and the partition threshold calculated in the previous iteration is less than the convergence threshold, and the final partition threshold is used as the optimal threshold;

[0058] S3105. The area in the backscattering coefficient image where the grayscale value is lower than the optimal threshold is taken as the preliminary water body result.

[0059] Furthermore, in S3102, the weighted average grayscale value u of the background area a The calculation formula is:

[0060]

[0061] Where T1 represents the division threshold, w(i) represents the pixel weight, f(x,y) represents the SDWI water index image, x represents the horizontal coordinate of each pixel on the two-dimensional remote sensing image, and y represents the vertical coordinate of each pixel on the two-dimensional remote sensing image.

[0062] In S3102, the weighted average gray value u of the foreground area b The calculation formula is:

[0063]

[0064] Furthermore, in S4, the interference coherence coefficient image is segmented using the optimal threshold to obtain the auxiliary extraction result of the water body area, and the preliminary water body extraction result of the same month is fused with the auxiliary extraction result of the water body area to obtain the fused water body extraction result.

[0065] Furthermore, in S5, the fused water body extraction results are subjected to morphological filtering, median filtering, and connected domain elimination to obtain the final water body range.

[0066] The beneficial effects of the present invention are: the weighted iterative threshold segmentation algorithm adopted by the present invention can more accurately find the appropriate segmentation threshold compared to the OTSU algorithm, exclude some weak object areas in the image, and at the same time utilize the interference coherence coefficient of the long time series SAR image to reduce the influence of submerged plants and sediment on the definition of the water edge; the present invention can not only accurately calculate the segmentation threshold, but also avoid the influence of submerged plants and sediment on the extraction results, thereby improving the accuracy of water area extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flow chart of the water area extraction method that integrates radar backscatter and interferometric coherence coefficient;

[0068] Figure 2 is the calculation result of SDWI water index;

[0069] Figure 3 This is a diagram of preliminary water extraction results according to an embodiment of the present invention;

[0070] Figure 4 This is a fusion water body result diagram of an embodiment of the present invention;

[0071] Figure 5 is the morphological filtering result of an embodiment of the present invention;

[0072] Figure 6 This is the final water body extraction result diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0074] like Figure 1 As shown, the present invention provides a water area extraction method that integrates radar backscatter and interference coherence coefficient, comprising the following steps:

[0075] S1. Acquire polarimetric SAR images and preprocess the polarimetric SAR images to obtain backscattering coefficient images and interferometric coherence coefficient images;

[0076] S2. Calculate the SDWI water index based on the backscatter coefficient images of different polarization modes;

[0077] S3. According to the SDWI water body index, the iterative threshold segmentation method is used to determine the preliminary extraction results of the water body;

[0078] S4. Based on the interference coherence coefficient image, the preliminary water body extraction results are fused to generate a fused water body extraction result;

[0079] S5. Refine the fused water body extraction results to obtain the final water body range.

[0080] In the embodiment of the present invention, in S1, the polarimetric SAR image is preprocessed by orbit correction, radiation correction, TOPS SAR image de-skewing, cropping, multi-viewing, DN value conversion to dB, and geocoding to obtain a backscatter coefficient image;

[0081] In S1, the polarimetric SAR image is preprocessed through orbit correction, radiation correction, TOPS SAR image segmentation, reverse geocoding, TOPS SAR image de-skewing, TOPS SAR image stitching, multi-viewing, interferometry, cropping and geocoding to obtain the interferometric coherence coefficient image.

[0082] In the embodiment of the present invention, in S2, the SDWI water index K SDWI The calculation formula is:

[0083] K SDWI =ln(10*VV*VH);

[0084] Where VV represents the backscatter coefficient of vertical polarization mode, and VH represents the backscatter coefficient of cross-polarization mode.

[0085] In this embodiment of the present invention, S3 includes the following sub-steps:

[0086] S31, dividing the grayscale image corresponding to the SAR image into several gray levels, and calculating the probability of each gray level;

[0087] S32. Calculate the background class probability and the foreground class probability according to the probability of each gray level;

[0088] Assume that the image segmentation threshold is k, and the grayscale values less than the threshold k are classified as background class a, and the grayscale values greater than the threshold k are classified as foreground class b.

[0089] S33, calculating the average grayscale value of the background class and the average grayscale value of the foreground class according to the background class probability and the foreground class probability;

[0090] S34, calculating the variance between different classes based on the background class probability, the foreground class probability, the average grayscale value of the background class, and the average grayscale value of the foreground class;

[0091] S35, calculating the variance between different elements in the background class and the variance between different elements in the foreground class based on the background class probability, the foreground class probability, the average grayscale value of the background class, and the average grayscale value of the foreground class;

[0092] S36. Calculate the ratio of the sum of the between-class variance and the within-class variance based on the variances between different classes, the variances between different elements in the background class, and the variances between different elements in the foreground class.

[0093] S37, repeat S32-S36 to obtain the ratio of the sum of the inter-class variance and the intra-class variance of each grayscale value, and extract the grayscale value corresponding to the maximum ratio;

[0094] S38, calculating the sum of the entropies of each gray value, and extracting the gray value corresponding to the maximum entropy sum;

[0095] S39, calculating a partition threshold according to the grayscale value corresponding to the maximum ratio and the grayscale value corresponding to the sum of the maximum entropy;

[0096] S310: Determine preliminary water body extraction results based on the division threshold and the SDWI water body index.

[0097] In the embodiment of the present invention, in S31, the probability P of the i-th gray level i The calculation formula is:

[0098]

[0099] Where n i represents the number of pixels with gray value i in the image, and N represents the total number of pixels in the grayscale image;

[0100] In S32, the background class probability P a The calculation formula for (k) is:

[0101]

[0102] Where k represents the segmentation threshold of the grayscale image;

[0103] In S32, the foreground class probability P b (k) calculation formula;

[0104]

[0105] Where L represents the number of gray levels of the grayscale image;

[0106] In S33, the average gray value m of the background class a The calculation formula for (k) is:

[0107]

[0108] In S33, the average gray value m of the foreground class b The calculation formula for (k) is:

[0109]

[0110] In S34, the variance between different classes σ0(k) 2 The calculation formula is:

[0111] σ0(k) 2 =P a (k)P b (k)(m a (k)-m b (k)) 2 ;

[0112] In S35, the variance σ between different elements in the background class a The calculation formula is:

[0113]

[0114] In S35, the variance σ between different elements in the foreground class bThe calculation formula is:

[0115]

[0116] In S36, the calculation formula for the ratio θ of the sum of the between-class variance and the within-class variance is:

[0117]

[0118] In S38, the calculation formula for the sum of grayscale entropy λ is:

[0119]

[0120] Where, ln(·) represents the logarithmic function;

[0121] In S39, the calculation formula of the division threshold T1 is:

[0122]

[0123] Where, t a Indicates the grayscale value corresponding to the maximum ratio, t b Represents the grayscale value corresponding to the sum of the maximum entropy.

[0124] In this embodiment of the present invention, S310 includes the following sub-steps:

[0125] S3101, dividing into background area and foreground area using a division threshold;

[0126] S3102, based on the SDWI water index, weighted average the background and foreground areas to obtain a weighted average grayscale value of the background area and a weighted average grayscale value of the foreground area;

[0127] S3103: Pixels with a grayscale value greater than the weighted average grayscale value of the background area and less than the weighted average grayscale value of the foreground area are considered as uncertain areas;

[0128] S3104, iteratively calculating the partition threshold of the uncertain region until the difference between the final partition threshold obtained and the partition threshold calculated in the previous iteration is less than the convergence threshold, and the final partition threshold is used as the optimal threshold;

[0129] S3105. The area in the backscattering coefficient image where the grayscale value is lower than the optimal threshold is taken as the preliminary water body result.

[0130] In the embodiment of the present invention, in S3102, the weighted average gray value u of the background area a The calculation formula is:

[0131]

[0132] Where T1 represents the division threshold, w(i) represents the pixel weight, f(x,y) represents the SDWI water index image, x represents the horizontal coordinate of each pixel on the two-dimensional remote sensing image, and y represents the vertical coordinate of each pixel on the two-dimensional remote sensing image.

[0133] In S3102, the weighted average gray value u of the foreground area b The calculation formula is:

[0134]

[0135] In an embodiment of the present invention, in S4, the interference coherence coefficient image is segmented using the optimal threshold to obtain the water body area auxiliary extraction result, and the water body preliminary extraction result of the same month is fused with the water body area auxiliary extraction result to obtain the fused water body extraction result.

[0136] In the embodiment of the present invention, in S5, morphological filtering, median filtering and connected domain elimination are performed on the fused water body extraction result to obtain the final water body range.

[0137] A disk-shaped structure element with a radius of 6 is used to perform a closing operation on the water body extraction result to smooth the boundary of the water body, thereby eliminating some small holes produced after segmentation and connecting minor fractures to make the image more complete and connected.

[0138] The present invention will be described below with reference to specific embodiments.

[0139] The present invention uses 226 periods of SLC data from 2016 to 2024 and processes them using SNAP10.0 software. The Sentinel-1A SLC data is preprocessed to obtain backscatter coefficient images and interference coherence coefficient images. The calculated SDWI water index results are as follows: Figure 2 The preliminary water body extraction results obtained by using the weighted iterative threshold segmentation algorithm for the SDWI water body index are shown in Figure 3 shown.

[0140] Threshold segmentation is performed on the interferometric coherence coefficient image to obtain auxiliary water body extraction results: Threshold segmentation of the interferometric coherence coefficient image reduces the interference of submerged plants and sediment on the extraction of water edges, which can cause inaccurate water body extraction using the OTSU algorithm combined with SDWI. The interferometric coherence coefficient measures the similarity between single-view complex images, that is, it describes the difference in the scattering characteristics of the ground objects between the two shots. Specifically, two SAR images from the same month are used as data to obtain the corresponding interferometric coherence coefficient images for that month. The interferometric coherence coefficient images with a median value less than 0.22 are identified as water bodies.

[0141] The preliminary water body extraction result is then fused with the auxiliary water body extraction result to obtain the water body extraction result: the preliminary water body extraction result using the backscatter coefficient and the auxiliary water body extraction result using the interference coherence coefficient are fused to obtain the fused water body extraction result. Affected by weak submerged plants and sediment, the echo signals generated by them and the water body echo signals are severely overlapped in the backscatter coefficient image, resulting in the inability to fully extract the water body using only the backscatter coefficient image. In order to solve this problem, this embodiment proposes to introduce radar interference coherence coefficient information for supplementation, perform threshold segmentation on the interference coherence coefficient image, extract the missing part of the water body, and fuse it with the water body result extracted using the backscatter coefficient. The fused water body extraction result is as follows Figure 4 shown.

[0142] Perform morphological filtering on the fused water body extraction results: Use a disk-shaped structuring element with a radius of 6 to perform a closing operation on the water body extraction results. The basic principle of morphological processing is to use structuring elements to collect information from images, and to generate output images by acting on the input image with structuring elements. Morphological closing operations can effectively fill small holes in the image and connect nearby disconnected crack edges. On the premise of keeping the water body area basically unchanged, it can significantly eliminate noise points caused by inaccurate threshold segmentation, thereby optimizing the fusion results of the water body area. This example performs a closing operation on the segmented image to smooth the boundaries of the water body, eliminate some small holes produced after segmentation, and connect fine fractures to make the image more complete and connected, thereby improving the accuracy of water body area extraction. The morphological filtering results are as follows: Figure 5 shown.

[0143] The results after morphological filtering are subjected to median filtering and connected domain elimination to obtain the final water body distribution result: In this embodiment, a 7×7 filter window is selected to perform median filtering on the image after morphological filtering to eliminate noise in the water body extraction result. On this basis, the connected domain is used to eliminate the connected components with less than 2000 pixels, eliminate the water areas with smaller areas, and retain the main water areas. The final water body result is as follows Figure 6 shown.

[0144] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A water area extraction method integrating radar backscatter and interferometric coherence coefficient, characterized in that: The following steps are involved: S1. Acquire polarimetric SAR images and preprocess the polarimetric SAR images to obtain backscattering coefficient images and interferometric coherence coefficient images; S2. Calculate the SDWI water index based on the backscatter coefficient images of different polarization modes; S3. According to the SDWI water body index, the iterative threshold segmentation method is used to determine the preliminary extraction results of the water body; S4. Based on the interference coherence coefficient image, the preliminary water body extraction results are fused to generate a fused water body extraction result; S5. Refine the fused water body extraction results to obtain the final water body range.

2. The water area extraction method integrating radar backscatter and interference coherence coefficient according to claim 1 is characterized in that: In S1, the polarimetric SAR image is preprocessed by orbit correction, radiation correction, TOPS SAR image de-skewing, cropping, multi-viewing, DN value conversion to dB and geocoding to obtain a backscatter coefficient image; In the above S1, the polarimetric SAR image is preprocessed by orbit correction, radiation correction, TOPS SAR image segmentation, reverse geocoding, TOPS SAR image de-skewing, TOPS SAR image stitching, multi-viewing, interferometry, cropping and geocoding to obtain an interferometric coherence coefficient image.

3. The water area extraction method integrating radar backscatter and interference coherence coefficient according to claim 1 is characterized in that: In the S2, SDWI water index K SDWI The calculation formula is: K SDWI =ln(10*VV*VH); Where VV represents the backscatter coefficient of vertical polarization mode, and VH represents the backscatter coefficient of cross-polarization mode.

4. The water area extraction method integrating radar backscatter and interference coherence coefficient according to claim 1 is characterized in that: The S3 includes the following sub-steps: S31, dividing the grayscale image corresponding to the SAR image into several gray levels, and calculating the probability of each gray level; S32. Calculate the background class probability and the foreground class probability according to the probability of each gray level; S33, calculating the average grayscale value of the background class and the average grayscale value of the foreground class according to the background class probability and the foreground class probability; S34, calculating the variance between different classes based on the background class probability, the foreground class probability, the average grayscale value of the background class, and the average grayscale value of the foreground class; S35, calculating the variance between different elements in the background class and the variance between different elements in the foreground class based on the background class probability, the foreground class probability, the average grayscale value of the background class, and the average grayscale value of the foreground class; S36. Calculate the ratio of the sum of the between-class variance and the within-class variance based on the variances between different classes, the variances between different elements in the background class, and the variances between different elements in the foreground class. S37, repeat S32-S36 to obtain the ratio of the sum of the inter-class variance and the intra-class variance of each grayscale value, and extract the grayscale value corresponding to the maximum ratio; S38, calculating the sum of the entropies of each gray value, and extracting the gray value corresponding to the maximum entropy sum; S39, calculating a partition threshold according to the grayscale value corresponding to the maximum ratio and the grayscale value corresponding to the sum of the maximum entropy; S310: Determine preliminary water body extraction results based on the division threshold and the SDWI water body index.

5. The water area extraction method integrating radar backscatter and interference coherence coefficient according to claim 4 is characterized in that: In S31, the probability P of the i-th gray level i The calculation formula is: Where n i represents the number of pixels with gray value i in the image, and N represents the total number of pixels in the grayscale image; In S32, the background class probability P a The calculation formula for (k) is: Where k represents the segmentation threshold of the grayscale image; In S32, the foreground class probability P b (k) calculation formula; Where L represents the number of gray levels of the grayscale image; In said S33, the average gray value m of the background class a The calculation formula for (k) is: In the above S33, the average gray value m of the foreground class b The calculation formula for (k) is: In S34, the variance between different classes σ0(k) 2 The calculation formula is: σ0(k) 2 =P a (k)P b (k)(m a (k)-m b (k)) 2 ; In S35, the variance σ between different elements in the background class a The calculation formula is: In S35, the variance σ between different elements in the foreground class b The calculation formula is: In S36, the calculation formula for the ratio θ of the sum of the between-class variance and the within-class variance is: In the above S38, the calculation formula of the entropy sum λ of the gray value is: Where, ln(·) represents the logarithmic function; In the above S39, the calculation formula of the division threshold T1 is: Where, t a Indicates the grayscale value corresponding to the maximum ratio, t b Represents the grayscale value corresponding to the sum of the maximum entropy.

6. The water area extraction method integrating radar backscatter and interference coherence coefficient according to claim 4 is characterized in that: The S310 includes the following sub-steps: S3101, dividing into background area and foreground area using a division threshold; S3102, based on the SDWI water index, weighted average the background and foreground areas to obtain a weighted average grayscale value of the background area and a weighted average grayscale value of the foreground area; S3103: Pixels with a grayscale value greater than the weighted average grayscale value of the background area and less than the weighted average grayscale value of the foreground area are considered as uncertain areas; S3104, iteratively calculating the partition threshold of the uncertain region until the difference between the final partition threshold obtained and the partition threshold calculated in the previous iteration is less than the convergence threshold, and the final partition threshold is used as the optimal threshold; S3105. The area in the backscattering coefficient image where the grayscale value is lower than the optimal threshold is taken as the preliminary water body result.

7. The water area extraction method integrating radar backscatter and interference coherence coefficient according to claim 6 is characterized in that: In the above S3102, the weighted average gray value u of the background area a The calculation formula is: Where T1 represents the division threshold, w(i) represents the pixel weight, f(x,y) represents the SDWI water index image, x represents the horizontal coordinate of each pixel on the two-dimensional remote sensing image, and y represents the vertical coordinate of each pixel on the two-dimensional remote sensing image. In the above S3102, the weighted average gray value u of the foreground area b The calculation formula is:

8. The water area extraction method integrating radar backscatter and interference coherence coefficient according to claim 1 is characterized in that: In S4, the interference coherence coefficient image is segmented using the optimal threshold to obtain the water body area auxiliary extraction result, and the water body preliminary extraction result and the water body area auxiliary extraction result of the same month are fused to obtain the fused water body extraction result.

9. The water area extraction method integrating radar backscatter and interference coherence coefficient according to claim 1 is characterized in that: In S5, the fused water body extraction result is subjected to morphological filtering, median filtering and connected domain elimination to obtain the final water body range.

Images