Flood inundation range identification methods, systems, electronic devices and storage media

Abstract

This invention provides a method, system, electronic device, and storage medium for identifying flood inundation areas, relating to the field of image recognition technology. The method includes: determining natural water body areas based on historical SAR images and preset thresholds; determining suspected water body areas based on real-time SAR images and preset thresholds; determining water surface fluctuation data based on real-time water level data; obtaining a corrected image based on the water surface fluctuation data and the real-time SAR image; obtaining a first water body area based on the corrected image and preset thresholds; superimposing and fusing the first water body area and the suspected water body area to obtain a water body coverage area; and removing natural water body areas from the water body coverage area to obtain the flood inundation area. Superimposing the first water body area onto the suspected water body area to obtain the water body coverage area reduces the identification error caused by water surface fluctuations, improves the accuracy of water body judgment, and removing natural water body areas to obtain the flood inundation area, thereby improving the accuracy of flood inundation area identification.

Description

Technical Field

[0001] This invention relates to the field of image recognition technology, and more specifically, to a method, system, electronic device, and storage medium for identifying the extent of flooding. Background Technology

[0002] Synthetic Aperture Radar (SAR) is an active microwave remote sensing technology that generates high-resolution images by emitting electromagnetic waves and receiving echo signals reflected from ground objects. Due to its unique imaging method and all-weather capability, SAR plays an irreplaceable role in fields such as flood monitoring, topographic mapping, and disaster emergency response.

[0003] Synthetic Aperture Radar (SAR) actively transmits microwaves through its antenna and receives echo signals reflected from ground features. Calm water surfaces create specular reflections with extremely low echo intensity, appearing as black in SAR images. Rough surfaces or vegetation scatter more strongly, appearing as bright colors in SAR images, thus identifying water bodies. However, thunderstorms are often accompanied by strong winds, disrupting the calm of water surfaces and potentially misclassifying them as non-water bodies, leading to reduced accuracy in identifying flood-prone areas. Summary of the Invention

[0004] The problem that this invention aims to solve is that existing synthetic aperture radar-based flooding range identification methods are prone to misjudging the water surface during thunderstorms and floods, resulting in reduced identification accuracy.

[0005] To address the aforementioned problems, in a first aspect, the present invention provides a method for identifying the extent of flooding, comprising:

[0006] The system acquires historical SAR images, real-time SAR images during floods, and real-time water level data collected by water level sensors, wherein the historical SAR images are SAR images acquired during windless conditions.

[0007] Based on the historical SAR images and preset thresholds, the natural water body area is determined;

[0008] Based on the real-time SAR images and preset thresholds, suspected water bodies are identified.

[0009] Based on the real-time water level data, determine the water surface fluctuation data;

[0010] Based on the water surface undulation data and real-time SAR images, a corrected image is obtained;

[0011] Based on the corrected image and the preset threshold, the first water body region is obtained;

[0012] The first water body area and the suspected water body area are superimposed and merged to obtain the water body coverage area;

[0013] The flood inundation range is obtained by removing the natural water body area from the water-covered area.

[0014] Optionally, the water surface fluctuation data includes the average water level fluctuation;

[0015] The step of determining the water surface fluctuation data based on the real-time water level data includes:

[0016] Based on the acquisition time of the real-time SAR image, real-time water level data of a preset duration is extracted;

[0017] Based on the captured real-time water level data, multiple water level peak-valley differences are obtained, and the average water level fluctuation is determined.

[0018] Optionally, obtaining the corrected image based on the water surface ripple data and the real-time SAR image includes:

[0019] By comparing the sizes of the natural water body area and the suspected water body area, the area to be identified as a water body is determined.

[0020] The area of ​​the water body to be processed is determined by expanding the range of the real-time SAR image to obtain the image to be processed.

[0021] Based on the water surface ripple data and the image to be processed, a corrected image is obtained.

[0022] Optionally, obtaining the corrected image based on the water surface ripple data and the image to be processed includes:

[0023] The fluctuation increment is determined based on the average water level fluctuation and the unit water level fluctuation.

[0024] Based on the image to be processed, the backscattering intensity in the region to be processed is subtracted from the fluctuation increment to obtain the corrected image.

[0025] Optionally, determining the fluctuation increment based on the average water level fluctuation and the unit water level fluctuation value includes:

[0026] Based on the unit water level fluctuation value, radar incident angle, radar wavelength, wave number, and dielectric constant of the natural water body, the backscattering intensity corresponding to the unit water level fluctuation value is obtained.

[0027] The fluctuation factor is determined based on the average water level fluctuation and the unit water level fluctuation.

[0028] The backscattering intensity corresponding to the average water level fluctuation is determined based on the fluctuation multiple and the backscattering intensity corresponding to the unit water level fluctuation value.

[0029] The fluctuation increment is determined based on the backscattering intensity corresponding to the average water level fluctuation and the standard backscattering intensity.

[0030] Optionally, the backscattering intensity corresponding to the unit water level fluctuation value is:

[0031] ,

[0032] , ,

[0033] in, This represents the backscattering intensity corresponding to a unit water level fluctuation. Represents the surface roughness spectrum; R represents the reflection coefficient; θ represents the dielectric constant of a natural water body; θ represents the radar incident angle. λ represents the wave number; h represents the unit water level fluctuation value; λ represents the radar wavelength.

[0034] Optionally, the fluctuation increment is:

[0035] ,

[0036] in, Indicates the increment of fluctuation. This represents the average water level fluctuation. This represents the standard backscattering intensity.

[0037] Secondly, the present invention also provides a flood inundation range identification system, comprising:

[0038] The data acquisition module is used to acquire historical SAR images, real-time SAR images during floods, and real-time water level data collected by water level sensors, wherein the historical SAR images are SAR images acquired when there is no wind.

[0039] The historical image recognition module is used to determine the natural water body area based on the historical SAR image and a preset threshold.

[0040] A real-time image recognition module is used to determine suspected water bodies based on the real-time SAR image and a preset threshold.

[0041] The water level fluctuation analysis module is used to determine water surface fluctuation data based on the real-time water level data.

[0042] An image correction module is used to obtain a corrected image based on the water surface ripple data and real-time SAR image;

[0043] The corrected image recognition module is used to obtain the first water body region based on the corrected image and a preset threshold.

[0044] The region fusion module is used to overlay and fuse the first water body region and the suspected water body region to obtain the water body coverage area;

[0045] The flood inundation range identification module is used to remove the natural water body area from the water body coverage area to obtain the flood inundation range.

[0046] Thirdly, the present invention provides an electronic device, including a memory and a processor;

[0047] The memory is used to store computer programs;

[0048] The processor is configured to implement the flood inundation range identification method as described in the first aspect when executing the computer program.

[0049] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the flood inundation range identification method as described in the first aspect.

[0050] This invention provides a method, system, electronic device, and storage medium for identifying flood inundation areas. Compared with existing technologies, it has the following advantages:

[0051] By using historical SAR images obtained during windless weather and preset thresholds, natural water bodies can be accurately identified. During floods, suspected water bodies can be identified first using real-time SAR images and preset thresholds. Real-time water level data is then collected by water level sensors placed around the natural water bodies to obtain water level fluctuation data. This water level fluctuation data is used to correct the real-time SAR images, resulting in a corrected image. Based on this corrected image and preset thresholds, water bodies are identified again to obtain the first water body area. This first water body area is then superimposed on the suspected water body areas to obtain the water body coverage area. This reduces the impact of potential over-correction, weakens the identification error caused by water level fluctuations, and improves the accuracy of water body identification. Finally, natural water bodies are removed, and the remaining area is the flood inundation range, thus improving the accuracy of flood inundation range identification. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart illustrating a method for identifying the flood inundation range provided in an embodiment of the present invention;

[0054] Figure 2This is a schematic diagram of a flood inundation range identification system provided in an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application are described clearly and completely. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0056] Synthetic Aperture Radar (SAR) is an active microwave remote sensing technology that generates high-resolution images by emitting electromagnetic waves and receiving the echo signals reflected from ground objects. Specifically, SAR actively emits microwaves through a radar antenna and then receives the echo signals reflected from ground objects. Different objects reflect microwaves differently, and by receiving the echo signals reflected from ground objects, different objects at different locations on the ground can be analyzed. For example, calm water surfaces create a specular reflection, resulting in extremely low echo intensity (appearing as black in the image); rough surfaces or vegetation scatter strongly, resulting in extremely high echo intensity (appearing as bright colors in the image).

[0057] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0058] like Figure 1 As shown in the embodiment of this application, a method for identifying the flood inundation range includes:

[0059] S1: Acquire historical SAR images, real-time SAR images during floods, and real-time water level data collected by water level sensors, wherein the historical SAR images are SAR images acquired when there is no wind.

[0060] S2: Determine the natural water body area based on the historical SAR images and preset thresholds.

[0061] S3: Based on the real-time SAR image and the preset threshold, determine the suspected water body area.

[0062] S4: Determine the water surface fluctuation data based on the real-time water level data.

[0063] S5: Obtain the corrected image based on the water surface ripple data and real-time SAR image.

[0064] S6: Based on the corrected image and the preset threshold, the first water body region is obtained.

[0065] S7: Overlay and merge the first water body area and the suspected water body area to obtain the water body coverage area.

[0066] S8: Based on the water body coverage area, the natural water body area is removed to obtain the flood inundation range.

[0067] In this embodiment, historical SAR images obtained during windless weather and preset thresholds can accurately determine natural water bodies (such as rivers, lakes, or streams). These natural water bodies represent areas not affected by flooding. During flooding, suspected water bodies can be identified using real-time SAR images and preset thresholds. However, due to strong winds, the water surface fluctuates, increasing surface roughness and altering reflection, potentially leading to misidentification as non-water bodies, resulting in less accurate identification. By setting up water level sensors around natural water bodies, real-time water level data can be collected, thus obtaining water level fluctuation data (such as peaks, troughs, or peak-to-trough differences). The real-time SAR image is then corrected using the water surface fluctuation data to obtain a corrected image. At this point, the backscatter intensity of the corrected image is corrected, and water bodies are identified again based on the corrected image and a preset threshold to obtain the first water body area. The first water body area is then superimposed on the suspected water body area to obtain the water body coverage area. This reduces the impact of possible over-correction, weakens the identification error caused by water surface fluctuations, and improves the accuracy of water body judgment. After removing the natural water body area, the remaining area is the flood inundation range, thereby improving the accuracy of flood inundation range identification.

[0068] The following is a detailed description of each step.

[0069] S1: Acquire historical SAR images, real-time SAR images during floods, and real-time water level data collected by water level sensors, wherein the historical SAR images are SAR images acquired when there is no wind.

[0070] Specifically, water level sensors are deployed around natural water bodies, such as along the edge of a riverbank. The sensors are also deployed at a certain height vertically along the riverbank, giving them a large monitoring range. They can monitor water level data from the riverbank to a specified distance above the ground, such as 0.5m or 1m.

[0071] S2: Determine the natural water body area based on the historical SAR images and preset thresholds.

[0072] S3: Based on the real-time SAR image and the preset threshold, determine the suspected water body area.

[0073] Specifically, receiving echo signals reflected from ground objects can generate high-resolution images. Different objects or surfaces with different roughness exhibit varying backscattering intensities to microwaves. By using backscattering intensity and a preset threshold, a threshold segmentation method is employed to determine whether an area is water or non-water. For example, if the preset threshold is set to -20 dB, areas with backscattering intensity greater than -20 dB are classified as non-water, while areas with backscattering intensity less than or equal to -20 dB are classified as water. Based on this, natural water bodies in historical SAR images and suspected water bodies in real-time SAR images can be identified.

[0074] S4: Determine the water surface fluctuation data based on the real-time water level data. The water surface fluctuation data includes the average value of water level peaks and the average value of water level troughs.

[0075] S41: Based on the acquisition time of the real-time SAR image, extract real-time water level data for a preset duration.

[0076] Specifically, within a short period, the differences in water level fluctuations are not significant. To accurately obtain the water level fluctuations at the time of acquiring real-time SAR images, a time window can be extracted, starting from that moment. The data within this time window most closely approximates the water level fluctuations corresponding to the time of acquiring the real-time SAR image. By analyzing the extracted real-time water level data, the water level fluctuations at the current moment can be determined. This approach reduces the volume of data to be analyzed and improves the accuracy of the results. Water level sensors can monitor water level data every moment. By statistically analyzing these data points chronologically on a time axis, continuous water level fluctuations can be obtained, forming a water level fluctuation curve. From this curve, multiple water level peak values ​​and trough values ​​can be extracted.

[0077] S42: Based on the intercepted real-time water level data, obtain multiple water level peak-valley differences and determine the average water level fluctuation.

[0078] Specifically, by using a set of adjacent peak and trough values, a water level peak-trough difference can be obtained. By using multiple sets of peak and trough values, multiple water level peak-trough differences can be obtained. Then, the average value is taken to obtain the average water level peak-trough difference, which is the average water level fluctuation value. This value is the water surface fluctuation caused by strong winds blowing the water surface or water surface flow. This average water level fluctuation value indicates the degree of water surface undulation.

[0079] S5: Obtain the corrected image based on the water surface ripple data and real-time SAR image.

[0080] S51: Compare the size of the natural water body area and the suspected water body area to determine the water body area to be determined.

[0081] S52: Expand the range of the water body to be processed in the real-time SAR image to determine the area to be processed and obtain the image to be processed.

[0082] Specifically, due to strong winds, the suspected water body area identified by real-time SAR images may be smaller than the natural water body area. This could be because strong water surface fluctuations cause continuous, high-density undulations across the entire water surface, making it difficult to accurately identify the water body. In this case, the natural water body area can be used as the potential water body area. Conversely, when the suspected water body area is larger than or equal to the natural water body area, it can be directly used as the potential water body area. Then, based on the potential water body area, the area is expanded equidistantly around it at a preset distance; or, the maximum width of the potential water body area is calculated, and half of that maximum width is expanded equidistantly around it. This delineates the area to be processed in the real-time SAR image, resulting in the image to be processed. In subsequent correction processes, only the area to be processed needs to be corrected, reducing the amount of data processed and improving processing speed.

[0083] S53: Based on the water surface ripple data and the image to be processed, a corrected image is obtained.

[0084] S531: Determine the fluctuation increment corresponding to the average water level fluctuation based on the average water level fluctuation and the unit water level fluctuation value. The specific steps for determining the fluctuation increment are as follows.

[0085] The backscattering intensity corresponding to the unit water level fluctuation value is obtained based on the unit water level fluctuation value, radar incident angle, radar wavelength, wave number, and dielectric constant of the natural water body.

[0086] Specifically, when the water surface ripples, the increase in backscattering intensity is the result of the combined effect of the water surface roughness spectrum and the reflection coefficient. The roughness spectrum affects the scattering path and scattering angle of light by changing the microscopic and macroscopic structure of the water surface; the reflection coefficient determines the proportion of light energy involved in scattering and the degree of interference between reflected light and scattered light.

[0087] The backscattering intensity corresponding to the unit water level fluctuation value is:

[0088] ,

[0089] , ,

[0090] in, This represents the backscattering intensity corresponding to a unit water level fluctuation. Represents the surface roughness spectrum; R represents the reflection coefficient; θ represents the dielectric constant of a natural water body; θ represents the radar incident angle. λ represents the wave number; h represents the unit water level fluctuation value; λ represents the radar wavelength.

[0091] The fluctuation factor is determined based on the average water level fluctuation and the unit water level fluctuation.

[0092] The backscattering intensity corresponding to the average water level fluctuation is determined based on the fluctuation multiple and the backscattering intensity corresponding to the unit water level fluctuation value.

[0093] Specifically, as the water level fluctuation value increases, the backscattering intensity also increases. Moreover, the water level fluctuation value and the backscattering intensity corresponding to a unit water level fluctuation value increase approximately proportionally. Therefore, after calculating the backscattering intensity corresponding to a unit water level fluctuation value, the ratio between the average water level fluctuation value and the unit water level fluctuation value can be calculated to obtain the fluctuation multiple. Then, the fluctuation multiple can be multiplied by the backscattering intensity corresponding to a unit water level fluctuation value to obtain the backscattering intensity corresponding to the average water level fluctuation value.

[0094] The fluctuation increment is determined based on the backscattering intensity corresponding to the average water level fluctuation and the standard backscattering intensity. The fluctuation increment is obtained by subtracting the standard backscattering intensity from the backscattering intensity corresponding to the average water level fluctuation.

[0095] The fluctuation increment is:

[0096] ,

[0097] in, Indicates the increment of fluctuation. This represents the average water level fluctuation. This represents the standard backscattering intensity.

[0098] For example, on a calm water surface, h=0, only specular reflection occurs, and the standard backscattering intensity is... ≈-25 dB. Assume radar parameters: C-band (λ=5.6cm), VV polarization, incident angle θ=30°, water medium: fresh water (dielectric constant...). =80, Fresnel reflection coefficient R≈0.95), then ≈112 rad / m, when the unit water level fluctuation value h=1 cm, , ≈-16.8dB. If the average water level fluctuation is also 1cm, then the fluctuation increment is -16.8 - (-25) = 8.2dB. If the average water level fluctuation is 1.5cm, then the fluctuation increment is -16.8 × 1.5 - (-25) = -0.2dB.

[0099] S532: Based on the image to be processed, subtract the fluctuation increment from the backscattering intensity in the region to be processed to obtain the corrected image.

[0100] Specifically, the backscattering intensity corresponding to the region to be processed in the image to be processed is subtracted from the fluctuation increment to obtain the corrected backscattering intensity, which in turn forms a corrected image. In the corrected image, the area of ​​the dark region is increased.

[0101] S6: Based on the corrected image and the preset threshold, the first water body region is obtained.

[0102] Specifically, a first water body region is obtained based on the backscattering intensity in the corrected image and a preset threshold. Regions with backscattering intensity less than or equal to the preset threshold are identified as water bodies and constitute the first water body region.

[0103] S7: Overlay and merge the first water body area and the suspected water body area to obtain the water body coverage area.

[0104] Specifically, when overlaying the first water body region and the suspected water body region, it is equivalent to first calculating the union of the two regions. When there is a bright area (i.e., a region with backscattering intensity greater than a preset threshold) within the two water body regions, the area of ​​the bright area is calculated. If the area of ​​the bright area is less than the preset area, it indicates that the region may be an unidentified water body or a non-water body region surrounded by a water body region, and the bright area can be directly regarded as the water body coverage area. When the area of ​​the bright area is greater than or equal to the preset area, it indicates that the region is a large non-water body region surrounded by a water body region, and the boundary of the bright area is separately outlined as a non-water body region. When some boundaries of the two water body regions are not continuous, if the length of the discontinuous position is less than the preset length, the discontinuous position is directly connected. If the length of the discontinuous position is greater than or equal to the preset length, the discontinuous state is maintained, thereby smoothing the edge burrs of the water body coverage area. The obtained water body coverage area takes into account both the suspected water body area and the primary water body area. On the one hand, it ensures that the identified water body coverage area is based on the natural water body area and spreads out, avoiding the problem of the identified water body coverage area deviating from the natural water body area and causing obvious errors in the identification results. On the other hand, the primary water body area identified after correction may be over-corrected, resulting in the loss of some water bodies. However, the lost part may be the area included in the natural water body area. After the two are superimposed, the error caused by over-correction can be reduced and the accuracy of the identification results can be improved.

[0105] S8: Based on the water body coverage area, the natural water body area is removed to obtain the flood inundation range.

[0106] Specifically, in order to better show the extent of flooding, natural water bodies can be removed from the water-covered area. The excess part is the extent of flooding caused by the overflow of natural water bodies due to precipitation. This can better show the direction and distribution of the flood inundation area, so as to formulate reasonable rescue measures.

[0107] like Figure 2 As shown in the figure, an embodiment of this application provides a flood inundation range identification system, comprising:

[0108] The data acquisition module 100 is used to acquire historical SAR images, real-time SAR images during floods, and real-time water level data collected by water level sensors, wherein the historical SAR images are SAR images acquired when there is no wind.

[0109] The historical image recognition module 200 is used to determine the natural water body area based on the historical SAR image and a preset threshold.

[0110] The real-time image recognition module 300 is used to determine the suspected water body area based on the real-time SAR image and a preset threshold.

[0111] The water level fluctuation analysis module 400 is used to determine water surface fluctuation data based on the real-time water level data.

[0112] The image correction module 500 is used to obtain a corrected image based on the water surface ripple data and the real-time SAR image;

[0113] The image recognition module 600 is used to obtain the first water body region based on the corrected image and a preset threshold.

[0114] The region fusion module 700 is used to overlay and fuse the first water body region and the suspected water body region to obtain the water body coverage region.

[0115] The flood inundation range identification module 800 is used to remove the natural water body area from the water body coverage area to obtain the flood inundation range.

[0116] In this embodiment, the beneficial effects of the flood inundation range identification system are similar to those of the flood inundation range identification method described above, and will not be repeated here.

[0117] An electronic device provided in this application includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the flood inundation range identification method as described above when the computer program is executed.

[0118] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the flood inundation range identification method described above.

[0119] In this embodiment, the beneficial effects of the electronic device and the computer-readable storage medium are similar to those of the flood inundation range identification method described above, and will not be repeated here.

[0120] The present invention describes electronic devices that can serve as servers or clients of this application, which are examples of hardware devices that can be applied to various aspects of this application. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the application described and / or claimed herein.

[0121] Electronic devices include a computing unit that can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM can also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0122] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application according to actual needs. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units.

[0123] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0124] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for identifying the extent of flood inundation, characterized in that, include: The system acquires historical SAR images, real-time SAR images during floods, and real-time water level data collected by water level sensors, wherein the historical SAR images are SAR images acquired during windless conditions. Based on the historical SAR images and preset thresholds, the natural water body area is determined; Based on the real-time SAR images and preset thresholds, suspected water bodies are identified. Based on the real-time water level data, determine the water surface fluctuation data; Based on the water surface undulation data and real-time SAR images, a corrected image is obtained; Based on the corrected image and the preset threshold, the first water body region is obtained; The first water body area and the suspected water body area are superimposed and merged to obtain the water body coverage area; The flood inundation range is obtained by removing the natural water body area from the water body coverage area. The water surface fluctuation data includes the average water level fluctuation; The step of determining the water surface fluctuation data based on the real-time water level data includes: Based on the acquisition time of the real-time SAR image, real-time water level data of a preset duration is extracted; Based on the captured real-time water level data, multiple water level peak-valley differences are obtained, and the average water level fluctuation is determined. The process of obtaining the corrected image based on the water surface ripple data and real-time SAR image includes: By comparing the sizes of the natural water body area and the suspected water body area, the area to be identified as a water body is determined. The area of ​​the water body to be processed is determined by expanding the range of the real-time SAR image to obtain the image to be processed. Based on the water surface ripple data and the image to be processed, a corrected image is obtained; The process of obtaining the corrected image based on the water surface fluctuation data and the image to be processed includes: The fluctuation increment is determined based on the average water level fluctuation and the unit water level fluctuation. Based on the image to be processed, the backscattering intensity in the region to be processed is subtracted from the fluctuation increment to obtain the corrected image.

2. The flood inundation range identification method as described in claim 1, characterized in that, The determination of the fluctuation increment based on the average water level fluctuation and the unit water level fluctuation includes: Based on the unit water level fluctuation value, radar incident angle, radar wavelength, wave number, and dielectric constant of the natural water body, the backscattering intensity corresponding to the unit water level fluctuation value is obtained. The fluctuation factor is determined based on the average water level fluctuation and the unit water level fluctuation. The backscattering intensity corresponding to the average water level fluctuation is determined based on the fluctuation multiple and the backscattering intensity corresponding to the unit water level fluctuation value. The fluctuation increment is determined based on the backscattering intensity corresponding to the average water level fluctuation and the standard backscattering intensity.

3. The flood inundation range identification method as described in claim 2, characterized in that, The backscattering intensity corresponding to the unit water level fluctuation value is: ， ， ， in, This represents the backscattering intensity corresponding to a unit water level fluctuation. Represents the surface roughness spectrum; R represents the reflection coefficient; θ represents the dielectric constant of a natural water body; θ represents the radar incident angle. λ represents the wave number; h represents the unit water level fluctuation value; λ represents the radar wavelength.

4. The flood inundation range identification method as described in claim 2, characterized in that, The fluctuation increment is: ， in, Indicates the increment of fluctuation. represents the average water level fluctuation, and h represents the unit water level fluctuation value; This represents the backscattering intensity corresponding to a unit water level fluctuation. This represents the standard backscattering intensity.

5. A flood inundation range identification system, characterized in that, The flood inundation range identification system, which implements the flood inundation range identification method as described in any one of claims 1 to 4, comprises: The data acquisition module is used to acquire historical SAR images, real-time SAR images during floods, and real-time water level data collected by water level sensors, wherein the historical SAR images are SAR images acquired when there is no wind. The historical image recognition module is used to determine the natural water body area based on the historical SAR image and a preset threshold. A real-time image recognition module is used to determine suspected water bodies based on the real-time SAR image and a preset threshold. The water level fluctuation analysis module is used to determine water surface fluctuation data based on the real-time water level data. An image correction module is used to obtain a corrected image based on the water surface ripple data and real-time SAR image; The corrected image recognition module is used to obtain the first water body region based on the corrected image and a preset threshold. The region fusion module is used to overlay and fuse the first water body region and the suspected water body region to obtain the water body coverage area; The flood inundation range identification module is used to remove the natural water body area from the water body coverage area to obtain the flood inundation range.

6. An electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to implement the flood inundation range identification method as described in any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the flood inundation range identification method as described in any one of claims 1 to 4.

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