Flood disaster monitoring method based on threshold interval and neighborhood analysis

Abstract

The invention relates to the technical field of flood disaster monitoring, in particular to a flood disaster monitoring method based on threshold interval and neighborhood analysis. The method comprises the following steps: obtaining a flood disaster area polarized radar image and carrying out preprocessing to obtain radar backscattering coefficient wave band data; secondly, drawing a radar backscattering coefficient histogram, and obtaining a backscattering coefficient value of a peak point of the land and the water body through histogram double-peak identification; thirdly, calculating a threshold segmentation interval based on the peak point, segmenting the image into a water body, a land and a land-water mixed pixel, and obtaining a water body preliminary extraction result; finally, false water bodies in the water bodies are removed, neighborhood land and water attribute judgment is carried out on the land and water mixed pixels, and finally a flood submerging water body extraction result is obtained. The monitoring precision of the submerged water body is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of flood disaster monitoring, and in particular to a flood disaster monitoring method based on threshold interval and neighborhood analysis. Background Art

[0002] Remote sensing technology has great potential in extracting flood and inundation information from large-scale, complex surfaces. However, flood disasters are often accompanied by extreme weather, which causes optical remote sensing using visible light and near-infrared bands to be greatly affected by clouds and rain, making it impossible to obtain effective surface information.

[0003] Remote sensing technology is used as a daily monitoring method for flood disasters. The threshold segmentation method selects one or more thresholds for water and non-water body division through artificial experience, OTSU (Otsu's Method, maximum inter-class variance method), KI (Kittler-Illingworth Method, minimum error threshold method) method. Usually, only the value of the backscatter coefficient in the radar image is considered. For complex surface areas such as mountain shadows and high-density urban scenes, the noise impact of non-water objects with low backscatter coefficients such as shadows, roads, and airports on water body extraction is not considered. The spatial distribution information contained in radar remote sensing images cannot be fully utilized, and the application effect is not good in complex scenes. At present, among the current high-precision flood disaster monitoring methods, machine learning methods rely on a large number of sample sets to build, with high computational complexity and poor real-time performance, which is difficult to meet the rapid response requirements of flood monitoring; the region growing algorithm is sensitive to noise and has a large amount of computation, and has poor effect on complex image processing; change detection and time series analysis methods have a lot of input image data and are greatly affected by coherent speckle noise. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a flood disaster monitoring method based on threshold interval and neighborhood analysis to solve at least one of the above technical problems.

[0005] To achieve the above purpose, a flood disaster monitoring method based on threshold interval and neighborhood analysis includes the following steps: Step S1: obtaining polarimetric radar images of flood-hit areas; preprocessing polarimetric radar images of flood-hit areas to obtain radar backscatter coefficient band data; Step S2: Draw a histogram of the radar backscatter coefficient band data to obtain a radar backscatter coefficient histogram; perform histogram bimodal recognition on the polarization radar image of the flood-stricken area based on the radar backscatter coefficient histogram to obtain the backscatter coefficient value of the peak point of the land and the backscatter coefficient value of the peak point of the water body; Step S3: Calculate the threshold segmentation interval of the polarimetric radar image of the flood-hit area according to the backscatter coefficient value of the peak point of the land and the backscatter coefficient value of the peak point of the water body to obtain the radar image threshold segmentation interval; segment the polarimetric radar image of the flood-hit area into water body, land and mixed water and land pixels according to the radar image threshold segmentation interval to obtain a preliminary water body extraction result, wherein the preliminary water body extraction result includes water body, land and mixed water and land pixels; Step S4: remove pseudo water bodies from the water bodies in the preliminary water body extraction results, and identify the neighboring water and land attributes of the water-land mixed pixels in the preliminary water body extraction results, so as to obtain the flood-inundated water body extraction results.

[0006] Preferably, step S1 comprises the following steps: Step S11: collecting polarimetric radar images of the target flood-stricken area to obtain polarimetric radar images of the flood-stricken area; Step S12: performing multi-view processing and filtering on the polarization radar image of the flood-hit area to obtain a multi-view filtered image of the flood-hit area; Step S13: geocoding the multi-view filtered image of the flood-hit area to obtain a geocoded image of the flood-hit area; Step S14: performing radiometric calibration on the geocoded image of the flood-hit area to obtain a polarimetric radar image of the flood-hit area; Step S15: performing coherent polarization decomposition correction on the polarization radar image of the flood-stricken area to obtain radar backscatter coefficient band data.

[0007] Preferably, step S2 comprises the following steps: Step S21: extracting pixel values ​​from radar backscatter coefficient band data to obtain a one-dimensional array of backscatter coefficients; Step S22: plotting a histogram of the radar backscatter coefficient band data based on the one-dimensional array of the backscatter coefficient to obtain a radar backscatter coefficient histogram; Step S23: performing histogram double peak identification on the radar backscatter coefficient histogram to obtain the backscatter coefficient value of the land peak point and the backscatter coefficient value of the water body peak point.

[0008] Preferably, step S23 includes the following steps: Perform smooth curve extraction on the radar backscatter coefficient histogram to obtain a smooth curve of the backscatter coefficient histogram; Based on the backscatter coefficient histogram smoothing curve, the histogram double peaks of the multi-view filter image in the flood-hit area are identified to obtain the backscatter coefficient value of the peak point of the land and the backscatter coefficient value of the peak point of the water body; among them, the specific processing process of the histogram double peaks identification of the multi-view filter image in the flood-hit area based on the backscatter coefficient histogram smoothing curve is as follows: ; ; ; ; ; ; in, is the backscatter coefficient histogram smoothing curve, w is the filter window size, p is the polynomial order of the Savitzky-Golay filter, is the backscattering coefficient value with the largest function value in the extreme point, is the backscattering coefficient value with the second largest function value among the extreme points, is the backscatter coefficient value of the peak point on land, is the backscattering coefficient value at the peak point of the water body.

[0009] Preferably, step S3 comprises the following steps: Step S31: The backscatter coefficient value of the land peak point As the mean of the Gaussian distribution of land, the backscatter coefficient value of the peak point of water body as the Gaussian distribution mean of the water body, and record the pixel values ​​in the radar backscatter coefficient band data that are less than the Gaussian distribution mean of the water body as the first candidate pixel set, and record the pixel values ​​in the radar backscatter coefficient band data that are greater than the Gaussian distribution mean of the land as the second candidate pixel set; Step S32: Calculate the standard deviation of the first candidate pixel set to obtain the standard deviation of the water body Gaussian distribution , and calculate the standard deviation of the second candidate pixel set to obtain the standard deviation of the land Gaussian distribution ; Step S33: Using 4 times the standard deviation as the initial segmentation interval of the polarization radar image of the flood-hit area, the lower limit of the initial segmentation interval is the Gaussian distribution mean of the water body + 4 times the Gaussian distribution standard deviation of the water body , the upper limit of the initial segmentation interval is the mean of the Gaussian distribution of the land 4 times the standard deviation of the land Gaussian distribution , that is, the initial segmentation interval is , determine the initial segmentation interval as Is the lower limit of the interval in greater than the upper limit of the interval? If so, reduce the initial segmentation interval by 0.5 times the standard deviation and recalculate the segmentation interval until the lower limit of the segmentation interval is less than the upper limit of the segmentation interval, so that the radar image threshold segmentation interval is obtained as ,k={4,3.5,3,2.5,2,1.5,1,0}; Step S34: segmenting the polarization radar image of the flood-stricken area into water body, land and water-land mixed pixels according to the radar image threshold segmentation interval to obtain a preliminary water body extraction result, wherein the preliminary water body extraction result includes water body, land and water-land mixed pixels.

[0010] Preferably, step S34 includes the following steps: Assume that the radar backscatter coefficient band data is I, and the preliminary water body extraction result is , the specific processing process of water body, land and water-land mixed pixel segmentation is as follows: .

[0011] Preferably, step S4 comprises the following steps: Step S41: extracting DEM values ​​and generating histograms for water bodies in the preliminary water body extraction results to obtain a DEM histogram of water body pixels; The preset lognormal distribution function is used to fit the DEM histogram of water body pixels to the lognormal distribution, and the DEM value of the cumulative distribution probability curve of the preset lognormal distribution function at 90% is calculated to obtain the water body DEM threshold. The water body DEM threshold is recorded as ; Among them, the probability density function and cumulative distribution function formulas of the preset log-normal distribution function are as follows: ; ; in, is the mean value of the water pixel DEM after logarithmic transformation, is the standard deviation of the water pixel DEM after logarithmic transformation, is the cumulative distribution function of the standard normal distribution; Step S42: The water body is higher than The water pixels are recorded as high-altitude water bodies. The pseudo water bodies in the high-altitude areas in the preliminary water body extraction results are eliminated, and the neighborhood water and land attributes of the water-land mixed pixels in the preliminary water body extraction results are judged to obtain the flood-inundated water body extraction results.

[0012] Preferably, the water body is above The water pixels are recorded as high-altitude water bodies. The specific process of removing pseudo water bodies in high-altitude water bodies in the preliminary water body extraction results is as follows: With the water body pixel in the high altitude area as the center, a neighborhood window is set with a size of N. The mountain shadow index of the water body pixel in the high altitude area is defined as MSI. When the MSI is greater than 5, the water body pixel in the high altitude area is judged to be a mountain shadow pixel. When the MSI is less than or equal to 5, the water body pixel in the high altitude area is judged to be a real water body. The MSI calculation formula is as follows: ; ; ; in, is the number of mixed pixels in the neighborhood window, is the number of water pixels in the neighborhood window, It is the maximum value of the normalized DEM in the neighborhood window.

[0013] Preferably, the specific process of performing neighborhood water and land attribute discrimination on the water-land mixed pixels in the preliminary water body extraction result is as follows: Assume that the neighborhood window size in the water-land mixed pixel is S, and calculate the number of water pixels and the number of land pixels in the neighborhood window S based on the water-land mixed pixel. According to the attribute discrimination rule of the water-land mixed pixel, the mixed pixel attribute in the water-land mixed pixel is judged. The attribute discrimination rule of the water-land mixed pixel is as follows: L(i,j)= −1−W(i,j); ; Among them, S is the neighborhood window size of the water-land mixed pixel, and the total number of pixels in the neighborhood is −1, L(i,j) is the number of land pixels, W(i,j) is the number of water pixels, i is the row number of the mixed land and water pixel, and j is the column number of the mixed land and water pixel.

[0014] The present invention automatically sets the upper and lower limits of the threshold based on the water-land mixed Gaussian distribution characteristics of radar remote sensing images, and preliminarily divides radar images into three categories: water bodies, land, and water-land mixed pixels; by analyzing the distribution characteristics and altitude characteristics of water bodies and water-land mixed pixels in different spatial regions, the mountain shadow index (MSI) is constructed to effectively remove pseudo water bodies caused by mountain shadows; the water and land attributes of water-land mixed pixels are further identified by using the neighborhood analysis method, thereby overcoming the classification error caused by low-scattering objects and significantly improving the monitoring accuracy. This method has the characteristics of low degree of manual intervention, simple threshold interval setting, and strong adaptability. It has the advantages of rapid response and high-precision monitoring in flood disaster emergency monitoring, and has strong adaptability and robustness in applications with complex terrain and multi-source radar remote sensing. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Other features, objects and advantages of the present invention will become more apparent from the detailed description made with reference to the following drawings: Figure 1 A schematic flow chart of the steps of a flood disaster monitoring method based on threshold interval and neighborhood analysis according to an embodiment is shown.

[0016] Figure 2 A schematic diagram of the backscatter coefficient image of the HH and HV polarization modes of the GF-3 according to an embodiment is shown.

[0017] Figure 3 A schematic diagram of backscatter coefficients of peak points of water and land distribution histograms of HH and HV band images in one embodiment is shown.

[0018] Figure 4 A schematic diagram of the threshold segmentation interval of the HH band of the GF-3 signal signal according to an embodiment is shown.

[0019] Figure 5 A schematic diagram of the GF-3 HV band threshold segmentation interval according to an embodiment is shown.

[0020] Figure 6 A schematic diagram of preliminary water extraction results according to an embodiment is shown.

[0021] Figure 7 A schematic diagram of a DEM data distribution histogram of water body pixels according to an embodiment is shown.

[0022] Figure 8 A schematic diagram showing the division results of a small number of water bodies in high altitude areas according to an embodiment is shown.

[0023] Fig. 9 A schematic diagram of the effect of removing mountain shadows from a preliminary extraction result of an embodiment is shown.

[0024] Fig.10 A schematic diagram of flood-inundated water body extraction results according to an embodiment is shown. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 The present invention further describes the technology of the present invention by taking the domestic dual-polarization GF-3 radar image as an example. The specific implementation method includes the following steps: Preferably, step S1 comprises the following steps: Step S11: collecting polarimetric radar images of the target flood-stricken area to obtain polarimetric radar images of the flood-stricken area; Step S12: performing multi-view processing and filtering on the polarization radar image of the flood-hit area to obtain a multi-view filtered image of the flood-hit area; Step S13: geocoding the multi-view filtered image of the flood-hit area to obtain a geocoded image of the flood-hit area; Step S14: performing radiometric calibration on the geocoded image of the flood-hit area to obtain a polarimetric radar image of the flood-hit area; Step S15: performing coherent polarization decomposition correction on the polarization radar image of the flood-stricken area to obtain radar backscatter coefficient band data.

[0029] In this embodiment, HH (Horizontal-Horizontal Polarization) and HV (Horizontal-Vertical Polarization) dual-polarization GF-3 radar remote sensing images of the target flood-stricken area are obtained, and the acquired radar remote sensing images are preprocessed by multi-viewing, geocoding, radiation calibration, filtering and cropping to obtain backscatter coefficient images in HH and HV polarization modes. The image size is 3600 2700, see Figure 2 .

[0030] Preferably, step S2 comprises the following steps: Step S21: extracting pixel values ​​from radar backscatter coefficient band data to obtain a one-dimensional array of backscatter coefficients; Step S22: plotting a histogram of the radar backscatter coefficient band data based on the one-dimensional array of the backscatter coefficient to obtain a radar backscatter coefficient histogram; Step S23: performing histogram double peak identification on the radar backscatter coefficient histogram to obtain the backscatter coefficient value of the land peak point and the backscatter coefficient value of the water body peak point.

[0031] In this embodiment, the grayscale histograms of the backscatter coefficient images in the HH and HV polarization modes are statistically analyzed, and the Savitzky-Golay filter is used to obtain the histogram smoothing curve to obtain the backscatter coefficient values ​​of the peak points of the land and the peak points of the water body, see Figure 3 .

[0032] Preferably, step S23 includes the following steps: Perform smooth curve extraction on the radar backscatter coefficient histogram to obtain a smooth curve of the backscatter coefficient histogram; Based on the backscatter coefficient histogram smoothing curve, the histogram double peaks of the multi-view filter image in the flood-hit area are identified to obtain the backscatter coefficient value of the peak point of the land and the backscatter coefficient value of the peak point of the water body; among them, the specific processing process of the histogram double peaks identification of the multi-view filter image in the flood-hit area based on the backscatter coefficient histogram smoothing curve is as follows: ; ; ; ; ; ; in, is the backscatter coefficient histogram smoothing curve, w is the filter window size, p is the polynomial order of the Savitzky-Golay filter, is the backscattering coefficient value with the largest function value in the extreme point, is the backscattering coefficient value with the second largest function value among the extreme points, is the backscatter coefficient value of the peak point on land, is the backscattering coefficient value at the peak point of the water body.

[0033] In this embodiment, the backscatter coefficients of the peak points of the water and land distribution histograms are used. , , as the mean of the Gaussian distribution of water and land, respectively, and calculate the number of images in each band that is greater than The standard deviation of the pixel value is used as the standard deviation of the land Gaussian distribution , calculate the number of images in each band that is smaller than The standard deviation of the pixel value is used as the standard deviation of the water body Gaussian distribution The lower limit of the interval is the mean of the backscatter coefficient of the water body + 4 times the standard deviation, and the upper limit of the interval is the mean of the backscatter coefficient of the land 4 times the standard deviation, and finally the threshold segmentation interval in this example is obtained as follows Figure 3 , see the schematic diagram of the segmentation interval Figure 4 , Figure 5 .

[0034] Preferably, step S3 comprises the following steps: Step S31: The backscatter coefficient value of the land peak point As the mean of the Gaussian distribution of land, the backscatter coefficient value of the peak point of water body as the Gaussian distribution mean of the water body, and record the pixel values ​​in the radar backscatter coefficient band data that are less than the Gaussian distribution mean of the water body as the first candidate pixel set, and record the pixel values ​​in the radar backscatter coefficient band data that are greater than the Gaussian distribution mean of the land as the second candidate pixel set; Step S32: Calculate the standard deviation of the first candidate pixel set to obtain the standard deviation of the water body Gaussian distribution , and calculate the standard deviation of the second candidate pixel set to obtain the standard deviation of the land Gaussian distribution ; Step S33: Using 4 times the standard deviation as the initial segmentation interval of the polarization radar image of the flood-hit area, the lower limit of the initial segmentation interval is the Gaussian distribution mean of the water body + 4 times the Gaussian distribution standard deviation of the water body , the upper limit of the initial segmentation interval is the mean of the Gaussian distribution of the land 4 times the standard deviation of the land Gaussian distribution , that is, the initial segmentation interval is , determine the initial segmentation interval as Is the lower limit of the interval in greater than the upper limit of the interval? If so, reduce the initial segmentation interval by 0.5 times the standard deviation and recalculate the segmentation interval until the lower limit of the segmentation interval is less than the upper limit of the segmentation interval, so that the radar image threshold segmentation interval is obtained as ,k={4,3.5,3,2.5,2,1.5,1,0}; Step S34: segmenting the polarization radar image of the flood-stricken area into water body, land and water-land mixed pixels according to the radar image threshold segmentation interval to obtain a preliminary water body extraction result, wherein the preliminary water body extraction result includes water body, land and water-land mixed pixels.

[0035] In this embodiment, according to the radar image threshold segmentation interval range, the pixels in the backscatter coefficient image that are less than the threshold interval are divided into water bodies and assigned a value of 1, the pixels that are greater than the threshold interval are divided into land and assigned a value of 0, and the pixels within the threshold interval are divided into mixed water and land pixels and assigned a value of 2. The extraction results corresponding to the two polarization bands (HH polarization band and HV polarization band) are obtained, and the two extraction results are merged to obtain the preliminary extraction results of the water body, see Figure 6 .

[0036] Preferably, step S34 includes the following steps: Assume that the radar backscatter coefficient band data is I, and the preliminary water body extraction result is , the specific processing process of water body, land and water-land mixed pixel segmentation is as follows: .

[0037] Preferably, step S4 comprises the following steps: Step S41: extracting DEM values ​​and generating histograms for water bodies in the preliminary water body extraction results to obtain a DEM histogram of water body pixels; The preset lognormal distribution function is used to fit the DEM histogram of water body pixels to the lognormal distribution, and the DEM value of the cumulative distribution probability curve of the preset lognormal distribution function at 90% is calculated to obtain the water body DEM threshold. The water body DEM threshold is recorded as ; Among them, the probability density function and cumulative distribution function formulas of the preset log-normal distribution function are as follows: ; ; in, is the mean value of the water pixel DEM after logarithmic transformation, is the standard deviation of the water pixel DEM after logarithmic transformation, is the cumulative distribution function of the standard normal distribution; Step S42: The water body is higher than The water pixels are recorded as high-altitude water bodies. The pseudo water bodies in the high-altitude areas in the preliminary water body extraction results are eliminated, and the neighborhood water and land attributes of the water-land mixed pixels in the preliminary water body extraction results are judged to obtain the flood-inundated water body extraction results.

[0038] In this embodiment, the above image ( Figure 6 ) to remove the pseudo water body error caused by mountain shadows. The log-normal distribution function is used to simulate the DEM data distribution histogram of water body pixels (see Figure 7), whose cumulative distribution probability curve is at 90% of the DEM value =30.97m. Water bodies above this value are high-altitude water bodies. The results of high-altitude water body classification are shown in Figure 8 .

[0039] Preferably, the water body is above The water pixels are recorded as high-altitude water bodies. The specific process of removing pseudo water bodies in high-altitude water bodies in the preliminary water body extraction results is as follows: With the water body pixel in the high altitude area as the center, a neighborhood window is set with a size of N. The mountain shadow index of the water body pixel in the high altitude area is defined as MSI. When the MSI is greater than 5, the water body pixel in the high altitude area is judged to be a mountain shadow pixel. When the MSI is less than or equal to 5, the water body pixel in the high altitude area is judged to be a real water body. The MSI calculation formula is as follows: ; ; ; in, is the number of mixed pixels in the neighborhood window, is the number of water pixels in the neighborhood window, It is the maximum value of the normalized DEM in the neighborhood window.

[0040] In this embodiment, the neighborhood window size is set to 41 with the water pixel in the high altitude area as the center. The larger window setting is conducive to processing larger mountain shadow patches. The mountain shadow index MSI is calculated based on DEM, water-land mixed pixels and water pixels. The water pixels with MSI values ​​greater than 5 in the preliminary water extraction results are judged as mountain shadows and assigned a value of 0. When the shadow patches are large, the shadows can be removed by iteratively calculating the MSI multiple times. The mountain shadow removal results are shown in Fig. 9 .

[0041] Preferably, the specific process of performing neighborhood water and land attribute discrimination on the water-land mixed pixels in the preliminary water body extraction result is as follows: Assume that the neighborhood window size in the water-land mixed pixel is S, and calculate the number of water pixels and the number of land pixels in the neighborhood window S based on the water-land mixed pixel. According to the attribute discrimination rule of the water-land mixed pixel, the mixed pixel attribute in the water-land mixed pixel is judged. The attribute discrimination rule of the water-land mixed pixel is as follows: L(i,j)= −1−W(i,j); ; Among them, S is the neighborhood window size of the water-land mixed pixel, and the total number of pixels in the neighborhood is −1, L(i,j) is the number of land pixels, W(i,j) is the number of water pixels, i is the row number of the mixed land and water pixel, and j is the column number of the mixed land and water pixel.

[0042] In this embodiment, the neighborhood window size S of the water-land mixed pixel is set to 3, the number of water pixels and the number of land pixels in the neighborhood window are calculated, and the attributes of the mixed pixels are judged according to the attribute discrimination rules of the water-land mixed pixels to obtain the flood submerged water body extraction result, see Fig.10 .

[0043] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0044] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A flood disaster monitoring method based on threshold interval and neighborhood analysis, characterized in that: The following steps are involved: Step S1: obtaining polarimetric radar images of flood-hit areas; preprocessing polarimetric radar images of flood-hit areas to obtain radar backscatter coefficient band data; Step S2: plotting a histogram of the radar backscatter coefficient band data to obtain a radar backscatter coefficient histogram; Based on the radar backscatter coefficient histogram, the polarization radar image of the flood-stricken area is subjected to histogram bimodal recognition to obtain the backscatter coefficient value of the peak point on land and the peak point on water. Step S3: Calculate the threshold segmentation interval of the polarimetric radar image of the flood-hit area according to the backscatter coefficient value of the peak point of the land and the backscatter coefficient value of the peak point of the water body to obtain the radar image threshold segmentation interval; segment the polarimetric radar image of the flood-hit area into water body, land and mixed water and land pixels according to the radar image threshold segmentation interval to obtain a preliminary water body extraction result, wherein the preliminary water body extraction result includes water body, land and mixed water and land pixels; Step S4: remove pseudo water bodies from the water bodies in the preliminary water body extraction results, and identify the neighboring water and land attributes of the water-land mixed pixels in the preliminary water body extraction results, so as to obtain the flood-inundated water body extraction results.

2. The flood disaster monitoring method based on threshold interval and neighborhood analysis according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collecting polarimetric radar images of the target flood-stricken area to obtain polarimetric radar images of the flood-stricken area; Step S12: performing multi-view processing and filtering on the polarization radar image of the flood-hit area to obtain a multi-view filtered image of the flood-hit area; Step S13: geocoding the multi-view filtered image of the flood-hit area to obtain a geocoded image of the flood-hit area; Step S14: performing radiometric calibration on the geocoded image of the flood-hit area to obtain a polarimetric radar image of the flood-hit area; Step S15: performing coherent polarization decomposition correction on the polarization radar image of the flood-stricken area to obtain radar backscatter coefficient band data.

3. The flood disaster monitoring method based on threshold interval and neighborhood analysis according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: extracting pixel values ​​from radar backscatter coefficient band data to obtain a one-dimensional array of backscatter coefficients; Step S22: plotting a histogram of the radar backscatter coefficient band data based on the one-dimensional array of the backscatter coefficient to obtain a radar backscatter coefficient histogram; Step S23: performing histogram double peak identification on the radar backscatter coefficient histogram to obtain the backscatter coefficient value of the land peak point and the backscatter coefficient value of the water peak point.

4. The flood disaster monitoring method based on threshold interval and neighborhood analysis according to claim 3 is characterized in that: Step S23 includes the following steps: Perform smooth curve extraction on the radar backscatter coefficient histogram to obtain a smooth curve of the backscatter coefficient histogram; Based on the backscatter coefficient histogram smoothing curve, the histogram double peaks of the multi-view filter image in the flood-hit area are identified to obtain the backscatter coefficient value of the peak point of the land and the backscatter coefficient value of the peak point of the water body; among them, the specific processing process of the histogram double peaks identification of the multi-view filter image in the flood-hit area based on the backscatter coefficient histogram smoothing curve is as follows: ； ； ； ； ； ； in, is the backscatter coefficient histogram smoothing curve, w is the filter window size, p is the polynomial order of the Savitzky-Golay filter, is the backscattering coefficient value with the largest function value in the extreme point, is the backscattering coefficient value with the second largest function value among the extreme points, is the backscatter coefficient value of the peak point on land, is the backscattering coefficient value at the peak point of the water body.

5. The flood disaster monitoring method based on threshold interval and neighborhood analysis according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: The backscatter coefficient value of the land peak point As the mean of the Gaussian distribution of land, the backscatter coefficient value of the peak point of water body as the Gaussian distribution mean of the water body, and record the pixel values ​​in the radar backscatter coefficient band data that are less than the Gaussian distribution mean of the water body as the first candidate pixel set, and record the pixel values ​​in the radar backscatter coefficient band data that are greater than the Gaussian distribution mean of the land as the second candidate pixel set; Step S32: Calculate the standard deviation of the first candidate pixel set to obtain the standard deviation of the water body Gaussian distribution , and calculate the standard deviation of the second candidate pixel set to obtain the standard deviation of the land Gaussian distribution ; Step S33: Using 4 times the standard deviation as the initial segmentation interval of the polarization radar image of the flood-hit area, the lower limit of the initial segmentation interval is the Gaussian distribution mean of the water body + 4 times the Gaussian distribution standard deviation of the water body , the upper limit of the initial segmentation interval is the mean of the Gaussian distribution of the land 4 times the standard deviation of the land Gaussian distribution , that is, the initial segmentation interval is , determine the initial segmentation interval as Is the lower limit of the interval in greater than the upper limit of the interval? If so, reduce the initial segmentation interval by 0.5 times the standard deviation and recalculate the segmentation interval until the lower limit of the segmentation interval is less than the upper limit of the segmentation interval, so that the radar image threshold segmentation interval is obtained as ,k={4,3.5,3,2.5,2,1.5,1,0}; Step S34: segmenting the polarization radar image of the flood-stricken area into water body, land and water-land mixed pixels according to the radar image threshold segmentation interval to obtain a preliminary water body extraction result, wherein the preliminary water body extraction result includes water body, land and water-land mixed pixels.

6. The flood disaster monitoring method based on threshold interval and neighborhood analysis according to claim 5 is characterized in that: Step S34 includes the following steps: Assume that the radar backscatter coefficient band data is I, and the preliminary water body extraction result is , the specific processing process of water body, land and water-land mixed pixel segmentation is as follows: 。 7. The flood disaster monitoring method based on threshold interval and neighborhood analysis according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: extracting DEM values ​​and generating histograms for water bodies in the preliminary water body extraction results to obtain a DEM histogram of water body pixels; The preset lognormal distribution function is used to fit the DEM histogram of water body pixels to the lognormal distribution, and the DEM value of the cumulative distribution probability curve of the preset lognormal distribution function at 90% is calculated to obtain the water body DEM threshold. The water body DEM threshold is recorded as ; Among them, the probability density function and cumulative distribution function formulas of the preset log-normal distribution function are as follows: ； ； in, is the mean value of the water pixel DEM after logarithmic transformation, is the standard deviation of the water pixel DEM after logarithmic transformation, is the cumulative distribution function of the standard normal distribution; Step S42: The water body is higher than The water pixels are recorded as high-altitude water bodies. The pseudo water bodies in the high-altitude areas in the preliminary water body extraction results are eliminated, and the neighborhood water and land attributes of the water-land mixed pixels in the preliminary water body extraction results are judged to obtain the flood-inundated water body extraction results.

8. The flood disaster monitoring method based on threshold interval and neighborhood analysis according to claim 7 is characterized in that: The water body is higher than The water pixels are recorded as high-altitude water bodies. The specific process of removing pseudo water bodies in high-altitude water bodies in the preliminary water body extraction results is as follows: With the water body pixel in the high altitude area as the center, a neighborhood window is set with a size of N. The mountain shadow index of the water body pixel in the high altitude area is defined as MSI. When the MSI is greater than 5, the water body pixel in the high altitude area is judged to be a mountain shadow pixel. When the MSI is less than or equal to 5, the water body pixel in the high altitude area is judged to be a real water body. The MSI calculation formula is as follows: ； ； ； in, is the number of mixed pixels in the neighborhood window, is the number of water pixels in the neighborhood window, It is the maximum value of the normalized DEM in the neighborhood window.

9. The flood disaster monitoring method based on threshold interval and neighborhood analysis according to claim 7 is characterized in that: The specific process of identifying the neighborhood water and land attributes of the water-land mixed pixels in the preliminary water body extraction results is as follows: Assume that the neighborhood window size in the water-land mixed pixel is S, and calculate the number of water pixels and the number of land pixels in the neighborhood window S based on the water-land mixed pixel. According to the attribute discrimination rule of the water-land mixed pixel, the mixed pixel attribute in the water-land mixed pixel is judged. The attribute discrimination rule of the water-land mixed pixel is as follows: L(i,j)= −1−W(i,j); ； Among them, S is the neighborhood window size of the water-land mixed pixel, and the total number of pixels in the neighborhood is −1, L(i,j) is the number of land pixels, W(i,j) is the number of water pixels, i is the row number of the mixed land and water pixel, and j is the column number of the mixed land and water pixel.

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