Flood disaster range extraction method and device

Abstract

The invention discloses a flood disaster range extraction method and device. Performing geometric fine correction and 6S atmospheric correction on the satellite remote sensing image data of the target area; performing soil moisture inversion by using the satellite remote sensing image data after geometric fine correction and 6S atmospheric correction; and extracting boundary information of the wetland by using a soil moisture inversion result, and determining a flood disaster range according to the boundary information. According to the technical scheme, geometric fine correction, 6S atmospheric correction and soil moisture inversion are carried out on the TM data, wetland boundary information is extracted through the inversion result, the flood range of flood disasters can be determined more accurately, and compared with an existing method for extracting the water body submerging range based on visible light remote sensing images, the method has the advantages that the method is simple and convenient to operate. The method effectively overcomes the problem of extraction leakage or less extraction caused by vegetation information interference, and can still accurately reflect water body information especially under the condition of high vegetation coverage in a farmland mature season and the like, thereby realizing more accurate range evaluation of flood disasters.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural flood disaster monitoring, and in particular to a method and device for extracting a flood disaster range. Background Art

[0002] Flooding refers to the inundation and waterlogging of low-lying areas due to heavy rain, torrential downpours, or sustained rainfall. This primarily harms crop growth, causing yield reductions or even complete crop failure, and disrupting agricultural production and the normal development of other industries.

[0003] Existing methods for extracting flood inundation areas are typically based on radar and visible light data. Radar data offers the advantage of being unaffected by cloud cover when extracting water bodies, but visible light data, due to its abundant data resources and large number of satellites, offers advantages for long-term water series research and post-disaster high-resolution monitoring of water changes. Most methods for extracting water inundation areas from visible light remote sensing data use methods such as water index, band thresholding, and spectral mixture analysis.

[0004] When using visible light remote sensing images to extract the scope of water inundation, the interference of vegetation information is the most important information. For example, in the ripening season of farmland, due to the high vegetation coverage, water body information is not easy to fail to reflect, resulting in missed extraction or insufficient extraction. Summary of the Invention

[0005] The present invention provides a method and device for extracting a flood disaster range, so as to improve the accuracy of extracting the flood disaster range.

[0006] According to one aspect of the present invention, a method for extracting a flood disaster range is provided, comprising:

[0007] Perform geometric precision correction and 6S atmospheric correction on satellite remote sensing image data of the target area;

[0008] Soil moisture is retrieved using satellite remote sensing image data after geometric precision correction and 6S atmospheric correction;

[0009] The soil moisture inversion result is used to extract the boundary information of the wetland, and the flood disaster range is determined based on the boundary information.

[0010] Optionally, performing geometric precision correction on the satellite remote sensing impact data of the target area includes:

[0011] Selecting ground control points in the satellite remote sensing image data;

[0012] According to the geometric distortion characteristics of the satellite remote sensing image data and the sensor model used, the relationship between the image coordinates and the actual ground coordinates is determined by a mathematical model to establish a geometric correction model;

[0013] Using the ground control points, solving geometric correction parameters in the geometric correction model so that the geometric correction model fits the relationship between the image coordinates and the actual coordinates of the ground control points;

[0014] The satellite remote sensing image data is resampled according to the geometric correction parameters, and each pixel in the satellite remote sensing image data is remapped to a corrected image space position.

[0015] Optionally, performing 6S atmospheric correction on the satellite remote sensing impact data of the target area includes:

[0016] The 6S model is used to calculate the atmospheric factors affecting radiation, including atmospheric molecular absorption, aerosol scattering, and Rayleigh scattering. The atmospheric correction parameters are obtained by iteratively calculating the radiation transfer equation.

[0017] According to the atmospheric correction parameters, correction calculation is performed on each pixel of the satellite remote sensing image data, and the corrected ground reflectivity or emissivity is obtained by deducting the atmospheric path radiation from the apparent radiance of the pixel and dividing it by the atmospheric transmittance.

[0018] Optionally, the soil moisture inversion using the satellite remote sensing image data after geometric precision correction and 6S atmospheric correction includes:

[0019] Using the surface temperature inversion model, the surface temperature value Ts is determined through meteorological data;

[0020] Calculate the Normalized Difference Vegetation Index (NDVI) using remote sensing images;

[0021] Fitting the dry-wet edge equation based on the Ts and the NDVI, and calculating the drought index TVDI value by the Ts and the dry-wet edge equation;

[0022] The TVDI value is converted into soil relative humidity, wherein the relative humidity of the wet side soil is set to 100%, and the relative humidity of the dry side soil is calculated and averaged by establishing a relationship with the measured value to obtain the soil moisture inversion result.

[0023] Optionally, after obtaining the soil moisture inversion result, the method further includes:

[0024] The field-measured soil moisture value of the target area is compared with the soil moisture relative humidity result, and the inversion accuracy is evaluated by calculating the error and correlation coefficient;

[0025] If the accuracy meets the requirements, the soil moisture inversion results are used to extract the boundary information of the wetland.

[0026] Optionally, extracting wetland boundary information using soil moisture inversion results includes:

[0027] According to the soil moisture inversion result and in combination with the characteristics of wetland soil moisture, a soil moisture threshold is set;

[0028] The soil moisture data of the target area is analyzed, and the area enclosed by pixels that meet the soil moisture threshold is used as the boundary information.

[0029] Optionally, determining the flood disaster scope according to the boundary information includes:

[0030] Smoothing and repairing the boundary information by using image processing technology;

[0031] The boundary information is analyzed according to the terrain data and the vegetation type data to determine the scope of the flood disaster.

[0032] According to another aspect of the present invention, a flood disaster range extraction device is provided, comprising:

[0033] Data processing unit, used to perform geometric precision correction and 6S atmospheric correction on satellite remote sensing image data of the target area;

[0034] Soil moisture inversion unit, used to invert soil moisture using satellite remote sensing image data after geometric precision correction and 6S atmospheric correction;

[0035] The boundary information extraction unit is used to extract the boundary information of the wetland using the soil moisture inversion result, and determine the flood disaster range according to the boundary information.

[0036] According to another aspect of the present invention, an electronic device is provided, comprising:

[0037] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the flood disaster range extraction method described in any embodiment of the present invention.

[0038] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the flood disaster range extraction method described in any embodiment of the present invention when executed.

[0039] The technical solution of this embodiment of the present invention performs geometric and 6S atmospheric correction on TM data, inverts soil moisture, and uses the inversion results to extract wetland boundary information, enabling more accurate determination of flood inundation extents. Compared to existing methods for extracting water inundation extents based on visible light remote sensing imagery, this method effectively overcomes the problem of missed or under-extraction caused by vegetation interference. This solution accurately reflects water body information, particularly under conditions of high vegetation cover, such as during the mature agricultural season, thereby enabling more precise assessment of the extent of flood disasters.

[0040] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 This is a flow chart of a method for extracting a flood disaster range provided by the first embodiment of the present invention;

[0043] Figure 2 1 is a schematic diagram of the extraction results of the maximum and minimum values ​​of the surface temperature applicable to the first embodiment of the present invention;

[0044] Figure 3 This is a structural diagram of a flood disaster range extraction device provided by the second embodiment of the present invention;

[0045] Figure 4 It is a structural diagram of an electronic device for implementing the flood disaster range extraction method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only 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 ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0047] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0048] Example 1

[0049] Figure 1 This is a flow chart of a method for extracting the scope of a flood disaster provided by the first embodiment of the present invention. This embodiment is applicable to the case of extracting the scope of a flood disaster. The method can be executed by a flood disaster scope extraction device. The flood disaster scope extraction device can be implemented in the form of hardware and / or software. The flood disaster scope extraction device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0050] S110: Perform geometric precision correction and 6S atmospheric correction on the satellite remote sensing image data of the target area.

[0051] Geometric correction is a crucial step in remote sensing image processing. It corrects geometric distortion caused by various factors, ensuring that images accurately reflect the true geographic location and spatial relationships of features. Atmospheric gas molecules (such as oxygen, ozone, and water vapor) and aerosol particles absorb and scatter solar radiation, causing the radiation signals received by remote sensing sensors to attenuate and alter, failing to accurately reflect the spectral characteristics of features. 6S atmospheric correction simulates the atmospheric radiation transmission process, calculating and removing atmospheric effects, ensuring that image data more closely reflects the true spectral characteristics of features.

[0052] In an embodiment of the present invention, performing geometric precision correction on satellite remote sensing impact data of a target area includes:

[0053] Select ground control points from satellite remote sensing image data;

[0054] According to the geometric distortion characteristics of satellite remote sensing image data and the sensor model used, the relationship between image coordinates and actual ground coordinates is determined through a mathematical model to establish a geometric correction model;

[0055] Using the ground control points, solving the geometric correction parameters in the geometric correction model so that the geometric correction model fits the relationship between the image coordinates and the actual coordinates of the ground control points;

[0056] According to the geometric correction parameters, the satellite remote sensing image data is resampled, and each pixel in the satellite remote sensing image data is remapped to the corrected image space position.

[0057] Ground control points are distinct features that can be accurately identified both in the image and on the ground, such as road intersections and building corners. Control points should be evenly distributed across the entire image area, and their number should be sufficient. Generally, they should be distributed across areas of varying terrain and feature types to ensure calibration accuracy.

[0058] Based on the geometric distortion characteristics of the image and the sensor model used, an appropriate mathematical model is selected to describe the relationship between the image coordinates and the actual ground coordinates. Common models include polynomial models and collinear equation models.

[0059] Using the selected ground control points, the parameters in the correction model are solved by mathematical methods such as the least squares method, so that the model can best fit the relationship between the image coordinates and the actual coordinates of the control points.

[0060] Based on the calculated correction parameters, the original image is resampled, remapping each pixel to the corrected image space. Resampling methods include nearest neighbor, bilinear interpolation, and cubic convolution, each with varying processing speeds and image quality. The resulting geometrically corrected image is precisely aligned with its pixel coordinates.

[0061] In an embodiment of the present invention, 6S atmospheric correction is performed on the satellite remote sensing impact data of the target area, including:

[0062] The 6S model is used to calculate the atmospheric factors affecting radiation, including atmospheric molecular absorption, aerosol scattering, and Rayleigh scattering. The atmospheric correction parameters are obtained by iteratively calculating the radiation transfer equation.

[0063] According to the atmospheric correction parameters, correction calculation is performed on each pixel of the satellite remote sensing image data. The corrected ground reflectivity or emissivity is obtained by deducting the atmospheric path radiation from the apparent radiation brightness of the pixel and dividing it by the atmospheric transmittance.

[0064] The 6S model describes the transmission of solar radiation through the atmosphere based on the radiative transfer equation. This equation accounts for complex processes such as atmospheric absorption, scattering, and multiple scattering. It simulates the total radiation received by the sensor by calculating components such as top-of-atmosphere (TOA) radiation, atmospheric path radiation, and radiation reflected from ground objects.

[0065] The 6S model includes parameterized descriptions of the optical properties of major atmospheric components, such as gas molecules and aerosols. For example, the gas absorption coefficients at different wavelengths and the aerosol scattering phase function are derived from extensive experimental measurements and theoretical research. Furthermore, the geometric relationship between the sun, target object, and sensor must be considered, including the solar zenith angle, observation zenith angle, and relative azimuth angle. These geometric parameters affect the atmospheric radiation transmission process and the illumination conditions of the object.

[0066] Input parameters for the 6S model include sensor type (used to determine the spectral response function), imaging time (to obtain the geometric position of the sun and satellite), geographic location (to determine atmospheric conditions and solar altitude), atmospheric mode (e.g., mid-latitude summer, tropical, etc.; different modes correspond to different atmospheric compositions and meteorological conditions), aerosol type (e.g., dust, marine aerosols), and aerosol optical depth. Some of these parameters can be obtained from auxiliary data, such as weather station data and aerosol monitoring data, while others must be set based on the characteristics of the study area and prior knowledge.

[0067] Using the input parameters, the 6S model calculates various atmospheric effects on radiation, including atmospheric molecular absorption, aerosol scattering, and Rayleigh scattering. By iteratively calculating the radiation transfer equation, it obtains the parameters required for correction, such as atmospheric transmittance and atmospheric path radiation.

[0068] Based on the calculated parameters, a correction calculation is performed on each pixel in the original remote sensing image. By deducting the atmospheric path radiation from the pixel's apparent radiance and dividing it by the atmospheric transmittance, the corrected reflectance or emissivity of the ground object is obtained. The corrected image can more accurately reflect the true spectral characteristics of the ground object.

[0069] S120, soil moisture inversion using satellite remote sensing image data after geometric precision correction and 6S atmospheric correction.

[0070] Soil moisture inversion refers to the process of indirectly inferring the moisture content in the soil through mathematical models or algorithms using various technical means and data. Preferably, after a series of processing (geometric precision correction, 6S atmospheric correction) of TM data, the temperature vegetation drought index method (TVDI) based on surface temperature (Ts) and normalized difference vegetation index (NDVI) is used to invert soil moisture. TVDI reflects the dry and wet conditions of the soil based on the two-dimensional feature space constructed based on Ts and NDVI. It believes that in this feature space, the value range of surface temperature and vegetation index and the relationship between them can characterize the change of soil moisture. By establishing the Ts-NDVI feature space, the surface temperature (Ts) of the pixel is compared with the dry edge (Tsmax) and wet edge (Tsmin) equations in the feature space, the TVDI value is calculated, and then the soil moisture is inverted.

[0071] In an embodiment of the present invention, soil moisture inversion is performed using satellite remote sensing image data after geometric precision correction and 6S atmospheric correction, including:

[0072] Using the surface temperature inversion model, the surface temperature value Ts is determined through meteorological data;

[0073] Calculate the Normalized Difference Vegetation Index (NDVI) using remote sensing images;

[0074] The dry-wet edge equation was fitted based on Ts and NDVI, and the drought index TVDI value was calculated by Ts and the dry-wet edge equation;

[0075] The TVDI value is converted into soil relative humidity, where the relative humidity of the wet side soil is set to 100%, and the relative humidity of the dry side soil is calculated and averaged by establishing a relationship with the measured value to obtain the soil moisture inversion result.

[0076] Ts and NDVI are two important parameters that can reflect the surface conditions. NDVI can characterize the growth and coverage of vegetation, and its value is related to the photosynthetic capacity and biomass of the vegetation; Ts is closely related to the energy balance of the surface, soil water evaporation and other processes. Soil moisture content affects vegetation growth (and thus NDVI) and surface temperature (Ts). When the soil is sufficiently hydrated, vegetation grows well and the NDVI value is high. At the same time, surface evaporation and heat dissipation are strong, and Ts is relatively low. Conversely, when the soil is insufficiently hydrated, vegetation growth is restricted, the NDVI value decreases, and the surface temperature rises due to reduced water evaporation.

[0077] TVDI is calculated from NDVI and Ts and is defined as:

[0078]

[0079] Where Ts is the surface temperature of any pixel; Tsmin is the wet edge equation of the feature space fitting; and Tsmax is the dry edge equation of the feature space fitting.

[0080] Usually, the surface parameters obtained from remote sensing data, such as surface temperature and vegetation index, do not cover the entire range from the dry edge to the wet edge, so the Ts-NDVI feature space is triangular or trapezoidal. The upper and lower sides of the triangle or trapezoid correspond to the wet edge (lowest temperature) and dry edge (highest temperature) of the temperature vegetation drought index model, respectively. If the scatter plot of the two-dimensional feature space established by Ts-NDVI is trapezoidal, the coordinates of the four vertices of the trapezoid calculated through relevant research show that under different vegetation coverage conditions, the wet edge equation Tsmin in the Ts-NVDI feature space will change accordingly due to different vegetation coverage. The linear fitting equations for the wet edge and dry edge are:

[0081] Tsmin=a1+b1NDVI;

[0082] Tsmax=a2+b2NDVI;

[0083] Among them, a and b are coefficients.

[0084] In the temperature-vegetation drought index model, the main challenge is fitting the dry-side and wet-side equations of the Ts-NDVI feature space. In this experiment, the two-dimensional feature space established by surface temperature (Ts) and the Normalized Difference Vegetation Index (NDVI) has a range of -1 to 1, and Ts values ​​range from 290.27K to 312.84K. A scatter plot of the feature space shows that the geometric characteristics meet the trapezoidal requirements. The Normalized Difference Vegetation Index (NDVI) is segmented into 0.01 scale intervals, and a computer algorithm is used to extract the maximum and minimum values ​​of the surface temperature (Ts) corresponding to each scale interval. Figure 2 This diagram illustrates the maximum and minimum surface temperature extraction results applicable to Example 1 of the present invention. Based on these extraction results, the dry-wet edge equations required for the Temperature Vegetation Drought Index (TVDI) model are fitted. This process is implemented using IDL programming. The NDVI value range is 0.15 to 0.80.

[0085] According to the fitting equations of the extracted characteristic points, the slopes of the dry-side equation and the wet-side equation are both negative, indicating that the surface temperature decreases with the increase of NDVI.

[0086] Ts = -26.661X + 318.03;

[0087] Ts = -8.5587X + 298.14;

[0088] Substitute the fitted Tsmax equation and Tsmin equation into the TVDI definition:

[0089]

[0090] The inversion result of the Temperature Vegetation Drought Index (TVDI) is only a quantitative index representing the dryness and wetness of the soil, with a value between 0 and 1. We need to convert it into relative soil humidity in order to compare it with the measured value. Convert the Temperature Vegetation Drought Index (TVDI) into relative soil humidity, that is, soil moisture percentage:

[0091] RSM=RSM W -TVDI*(RSM W -RSM D );

[0092] Where RSMW and RSMD are the relative soil water contents corresponding to the wet edge and dry edge in the characteristic space of the study area, respectively, among which RSMW has the largest value and RSMD has the smallest value.

[0093] Based on the distribution characteristics of the TS-NDVI feature space scatter plot, it is found that the temperature of the water body is mainly distributed at the lowest value, and the humidity of the water body can be considered saturated. Therefore, the relative humidity of the soil on the wet side is determined to be 100%. The relative humidity of the soil on the dry side can be obtained using the following formula:

[0094]

[0095] Yi is the measured relative soil humidity at that point; Xi is the corresponding TVDI value at that point. Average the calculated RSMDi values ​​to determine the relative soil humidity on the dry side. Combining the above formula yields the relative soil humidity value.

[0096] In an embodiment of the present invention, the method may further include:

[0097] The field-measured soil moisture values ​​in the target area were compared with the soil relative humidity results, and the inversion accuracy was evaluated by calculating the error and correlation coefficient.

[0098] If the accuracy meets the requirements, the soil moisture inversion results are used to extract the boundary information of the wetland.

[0099] Soil moisture is easily affected by environmental factors, and its correlation with remote sensing data is poor and unstable. Soil moisture at a depth of approximately 20 cm correlates well with imagery data. Relative soil moisture inversions using NOAA / AVHRR at depths of 10 cm, 20 cm, and 30 cm also concluded that soil moisture at 20 cm correlates best and most stably with remote sensing data. Therefore, when multispectral soil moisture inversion is performed, soil moisture at a depth of approximately 20 cm correlates well with remote sensing data. This paper also used measured soil moisture values ​​at a depth of 20 cm for verification.

[0100] Field-measured soil moisture values ​​at a depth of 20 cm are compared with the inversion results. The inversion accuracy is evaluated by calculating statistical indicators such as error (root mean square error, mean absolute error), and correlation coefficients. If the accuracy meets the requirements, the inverted soil moisture data can be used for subsequent wetland boundary extraction. If not, the cause needs to be analyzed and the inversion method needs to be improved.

[0101] S130. Extracting wetland boundary information using the soil moisture inversion result, and determining the flood disaster range based on the boundary information.

[0102] In an embodiment of the present invention, the boundary information of the wetland is extracted using the soil moisture inversion result, including:

[0103] According to the soil moisture inversion results and the characteristics of wetland soil moisture, the soil moisture threshold is set;

[0104] The soil moisture data of the target area is analyzed, and the area enclosed by pixels that meet the soil moisture threshold is used as boundary information.

[0105] Based on the inverted soil moisture relative humidity data and the characteristics of wetland soil moisture, an appropriate soil moisture threshold is set. Generally speaking, wetland soil moisture content is relatively high, and areas with soil moisture relative humidity above this threshold can be preliminarily identified as wetland areas. By analyzing soil moisture data for the entire study area, pixels that meet the threshold conditions are identified. The area formed by these pixels is the approximate extent of the wetland.

[0106] In an embodiment of the present invention, determining the flood disaster range based on boundary information includes:

[0107] The boundary information is smoothed and repaired by adopting image processing technology;

[0108] Analyze boundary information based on terrain data and vegetation type data to determine the scope of flood disasters.

[0109] The initially determined wetland boundaries may contain some inaccuracies or discontinuities and require further optimization and refinement. Image processing techniques, such as edge detection algorithms and morphological processing (such as dilation and erosion operations), can be used to smooth and repair the initially extracted boundaries to make them more consistent with the actual boundary characteristics of the wetland. At the same time, a comprehensive analysis can be conducted in combination with other auxiliary data (such as terrain data and vegetation type data) to further accurately define the wetland boundaries. For example, wetlands are usually located in low-lying areas. Topographic data can be used to exclude highland areas that are mistakenly identified as wetlands due to local soil moisture anomalies; the distribution characteristics of wetland vegetation types can be used to assist in judging the rationality of wetland boundaries.

[0110] Visualize the extracted wetland boundary information, such as by generating thematic maps that clearly display the wetland's extent and boundary shape. Validate the extracted results through field surveys, high-resolution image interpretation, or comparison with historical wetland boundary data. If significant deviations between the extracted results and the actual situation are found, adjust the data processing and boundary extraction process, adjusting parameters or methods, until accurate wetland boundary information is obtained.

[0111] Example 2

[0112] Figure 3 This is a schematic diagram of the structure of a flood disaster range extraction device provided by the second embodiment of the present invention. Figure 3 As shown, the device includes:

[0113] The data processing unit 310 is used to perform geometric precision correction and 6S atmospheric correction on the satellite remote sensing image data of the target area;

[0114] The soil moisture inversion unit 320 is used to perform soil moisture inversion using the satellite remote sensing image data after geometric precision correction and 6S atmospheric correction;

[0115] The boundary information extraction unit 330 is used to extract the boundary information of the wetland using the soil moisture inversion result, and determine the flood disaster range based on the boundary information.

[0116] Optionally, the data processing unit 310 is specifically configured to execute:

[0117] Selecting ground control points in the satellite remote sensing image data;

[0118] According to the geometric distortion characteristics of the satellite remote sensing image data and the sensor model used, the relationship between the image coordinates and the actual ground coordinates is determined by a mathematical model to establish a geometric correction model;

[0119] Using the ground control points, solving geometric correction parameters in the geometric correction model so that the geometric correction model fits the relationship between the image coordinates and the actual coordinates of the ground control points;

[0120] The satellite remote sensing image data is resampled according to the geometric correction parameters, and each pixel in the satellite remote sensing image data is remapped to a corrected image space position.

[0121] Optionally, the data processing unit 310 is specifically configured to execute:

[0122] The 6S model is used to calculate the atmospheric factors affecting radiation, including atmospheric molecular absorption, aerosol scattering, and Rayleigh scattering. The atmospheric correction parameters are obtained by iteratively calculating the radiation transfer equation.

[0123] According to the atmospheric correction parameters, correction calculation is performed on each pixel of the satellite remote sensing image data, and the corrected ground reflectivity or emissivity is obtained by deducting the atmospheric path radiation from the apparent radiance of the pixel and dividing it by the atmospheric transmittance.

[0124] Optionally, the soil moisture inversion unit 320 is specifically configured to perform:

[0125] Using the surface temperature inversion model, the surface temperature value Ts is determined through meteorological data;

[0126] Calculate the Normalized Difference Vegetation Index (NDVI) using remote sensing images;

[0127] Fitting the dry-wet edge equation based on the Ts and the NDVI, and calculating the drought index TVDI value by the Ts and the dry-wet edge equation;

[0128] The TVDI value is converted into soil relative humidity, wherein the relative humidity of the wet side soil is set to 100%, and the relative humidity of the dry side soil is calculated and averaged by establishing a relationship with the measured value to obtain the soil moisture inversion result.

[0129] Optionally, after obtaining the soil moisture inversion result, the soil moisture inversion unit 320 is further configured to execute:

[0130] The field-measured soil moisture value of the target area is compared with the soil moisture relative humidity result, and the inversion accuracy is evaluated by calculating the error and correlation coefficient;

[0131] If the accuracy meets the requirements, the soil moisture inversion results are used to extract the boundary information of the wetland.

[0132] Optionally, the boundary information extraction unit 330 is specifically configured to perform:

[0133] According to the soil moisture inversion result and in combination with the characteristics of wetland soil moisture, a soil moisture threshold is set;

[0134] The soil moisture data of the target area is analyzed, and the area enclosed by pixels that meet the soil moisture threshold is used as the boundary information.

[0135] Optionally, the boundary information extraction unit 330 is specifically configured to perform:

[0136] Smoothing and repairing the boundary information by using image processing technology;

[0137] The boundary information is analyzed according to the terrain data and the vegetation type data to determine the scope of the flood disaster.

[0138] The flood disaster range extraction device provided by the embodiment of the present invention can execute the flood disaster range extraction method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0139] Example 3

[0140] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) 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 present invention described and / or claimed herein.

[0141] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0142] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0143] Processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any other suitable processor, controller, microcontroller, etc. Processor 11 executes the various methods and processes described above, such as the flood disaster range extraction method.

[0144] In some embodiments, the flood hazard range extraction method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the flood hazard range extraction method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the flood hazard range extraction method in any other suitable manner (e.g., via firmware).

[0145] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0146] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0147] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0148] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0149] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0150] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0151] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0152] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for extracting the scope of flood disaster, characterized in that: include: Perform geometric precision correction and 6S atmospheric correction on satellite remote sensing image data of the target area; Soil moisture is retrieved using satellite remote sensing image data after geometric precision correction and 6S atmospheric correction; The soil moisture inversion result is used to extract the boundary information of the wetland, and the flood disaster range is determined based on the boundary information.

2. The method according to claim 1, characterized in that The geometric precision correction of the satellite remote sensing impact data of the target area includes: Selecting ground control points in the satellite remote sensing image data; According to the geometric distortion characteristics of the satellite remote sensing image data and the sensor model used, the relationship between the image coordinates and the actual ground coordinates is determined by a mathematical model to establish a geometric correction model; Using the ground control points, solving geometric correction parameters in the geometric correction model so that the geometric correction model fits the relationship between the image coordinates and the actual coordinates of the ground control points; The satellite remote sensing image data is resampled according to the geometric correction parameters, and each pixel in the satellite remote sensing image data is remapped to a corrected image space position.

3. The method according to claim 1, characterized in that The 6S atmospheric correction of the satellite remote sensing impact data of the target area includes: The 6S model is used to calculate the atmospheric factors affecting radiation, including atmospheric molecular absorption, aerosol scattering, and Rayleigh scattering. The atmospheric correction parameters are obtained by iteratively calculating the radiation transfer equation. According to the atmospheric correction parameters, correction calculation is performed on each pixel of the satellite remote sensing image data, and the corrected ground reflectivity or emissivity is obtained by deducting the atmospheric path radiation from the apparent radiance of the pixel and dividing it by the atmospheric transmittance.

4. The method according to claim 1, wherein The soil moisture inversion using the satellite remote sensing image data after geometric precision correction and 6S atmospheric correction includes: Using the surface temperature inversion model, the surface temperature value Ts is determined through meteorological data; Calculate the Normalized Difference Vegetation Index (NDVI) using remote sensing images; Fitting the dry-wet edge equation based on the Ts and the NDVI, and calculating the drought index TVDI value by the Ts and the dry-wet edge equation; The TVDI value is converted into soil relative humidity, wherein the relative humidity of the wet side soil is set to 100%, and the relative humidity of the dry side soil is calculated and averaged by establishing a relationship with the measured value to obtain the soil moisture inversion result.

5. The method according to claim 1, characterized in that After obtaining the soil moisture inversion result, the method further includes: The field-measured soil moisture value of the target area is compared with the soil moisture relative humidity result, and the inversion accuracy is evaluated by calculating the error and correlation coefficient; If the accuracy meets the requirements, the soil moisture inversion results are used to extract the boundary information of the wetland.

6. The method according to claim 1, characterized in that The method of extracting wetland boundary information using soil moisture inversion results includes: According to the soil moisture inversion result and in combination with the characteristics of wetland soil moisture, a soil moisture threshold is set; The soil moisture data of the target area is analyzed, and the area enclosed by pixels that meet the soil moisture threshold is used as the boundary information.

7. The method according to any one of claims 1 to 6, characterized in that: Determining the flood disaster scope according to the boundary information includes: Smoothing and repairing the boundary information by using image processing technology; The boundary information is analyzed according to the terrain data and the vegetation type data to determine the scope of the flood disaster.

8. A flood disaster range extraction device, characterized in that: include: Data processing unit, used to perform geometric precision correction and 6S atmospheric correction on satellite remote sensing image data of the target area; Soil moisture inversion unit, used to invert soil moisture using satellite remote sensing image data after geometric precision correction and 6S atmospheric correction; The boundary information extraction unit is used to extract the boundary information of the wetland using the soil moisture inversion result, and determine the flood disaster range according to the boundary information.

9. An electronic device, characterized in that: The electronic device comprises: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a flood disaster range extraction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a flood disaster range extraction method according to any one of claims 1 to 7 when executed.

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