A flood monitoring and deduction method based on unmanned aerial vehicle time sequence radar remote sensing

Abstract

The application discloses a kind of based on unmanned aerial vehicle time sequence radar remote sensing's flood monitoring and deduction method, comprising: S1, through unmanned aerial vehicle radar remote sensing acquisition multi-source flood disaster high spatial resolution time sequence radar remote sensing data, and it is handled as the remote sensing data set of space reference consistent;S2, each time sequence radar remote sensing image in remote sensing data set is preprocessed;S3, to the time sequence radar remote sensing image after pre-processing is flooded area and non-flooded area segmentation, and remove small water body in flooded area, extract radar remote sensing high-resolution flooded range;S4, quantitatively extract flood evolution characteristic element, and combine digital elevation model and hydrological connectivity to current flooded range carries out flood disaster potential influence area deduction.The method of the application breaks through the problem that large-scale flood evolution process is difficult to dynamically monitor, and provides support for basin flood rapid emergency and risk transfer by forward deduction flood disaster potential influence area.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of flood disaster monitoring, and particularly relates to a flood monitoring and deduction method based on unmanned aerial vehicle (UAV) time-series radar remote sensing. BACKGROUND

[0002] Flood disaster is the natural disaster type with the highest occurrence frequency, the widest influence range and the largest loss in China. Flood disaster will cause large-area houses to be flooded, infrastructure to be damaged and crops to be affected, and will bring huge losses to human life and property safety and social and economic development.

[0003] UAV remote sensing has the advantages of large range, rapidness, macroscopicness and accuracy, and is an important means for monitoring the flood evolution process. The UAV optical remote sensing monitoring relies on visible light and near-infrared bands, and is easily affected by weather conditions. When the flood occurs, it is usually cloudy and rainy, and the optical remote sensing sensor cannot obtain clear ground images. Moreover, the optical image has certain limitations in water body recognition accuracy, especially in distinguishing shallow water areas, mixed pixel areas and water areas covered by vegetation, and the accuracy is poor.

[0004] Radar remote sensing has unique advantages. It uses microwave bands for detection, can penetrate clouds, is not limited by weather conditions, and can realize all-time and all-weather observation. UAV radar remote sensing has the characteristics of high spatial resolution and high temporal resolution, and can identify the surface flood inundation characteristics in detail.

[0005] With the continuous development of UAV network observation technology and radar sensors, multi-source collaborative UAV radar remote sensing data can provide high-frequency return, high spatial resolution and high-quality remote sensing image data, and provide important support for flood disaster emergency rapid monitoring and deduction. However, due to the current large noise of UAV radar remote sensing image, the complex backscattering characteristics and the complex shape of flooded water area, there is still a lack of effective algorithm for multi-time UAV radar remote sensing flood inundation monitoring. Accurate and timely flood monitoring and deduction is of great significance for disaster warning, emergency response and post-disaster recovery. In addition, current UAV remote sensing focuses on emergency monitoring, and lacks precise flood deduction.

[0006] In summary, developing a flood monitoring and deduction method based on UAV time-series radar remote sensing can effectively utilize the high temporal and spatial resolution advantages of multi-time radar remote sensing, overcome the shortcomings of traditional methods, and meet the urgent needs of flood disaster emergency. SUMMARY

[0007] In view of the above-mentioned deficiencies in the prior art, the flood monitoring and deduction method based on UAV time-series radar remote sensing provided by the application solves the problems that the existing related methods are difficult to dynamically monitor the large-scale flood evolution process and lack precise deduction.

[0008] To achieve the above-mentioned purposes, the technical scheme adopted by the present application is as follows: a flood monitoring and deduction method based on unmanned aerial vehicle time-series radar remote sensing, comprising the following steps:

[0009] S1, collecting multi-source flood disaster high spatial resolution time-series radar remote sensing data by unmanned aerial vehicle radar remote sensing, and processing the data into a remote sensing data set with consistent space-time reference;

[0010] S2, preprocessing each time-series radar remote sensing image in the remote sensing data set;

[0011] S3, segmenting the preprocessed time-series radar remote sensing image into flooded and non-flooded areas, removing small water bodies in the flooded area, and extracting radar remote sensing high-resolution flooded area;

[0012] S4, quantitatively extracting flood evolution characteristic elements, and combining digital elevation model and improved Manning formula to deduce the potential impact area of the current flooded area.

[0013] Further, in step S1, the processing of time-series radar remote sensing data comprises:

[0014] establishing an affine transformation model representing the transformation relationship between ground control point coordinates and image coordinates of time-series radar remote sensing data, and converting the image coordinates of time-series radar remote sensing data to actual geographic coordinates in the geodetic coordinate system;

[0015] and converting the digital quantization value of radar image in time-series radar remote sensing data to radar backscatter coefficient.

[0016] Further, step S2 comprises the following steps:

[0017] S21, performing cross-sensor radiometric normalization on each time-series radar remote sensing image;

[0018] S22, correcting the histogram of the time-series radar remote sensing image after radiometric normalization by using the percentile clipping method;

[0019] S23, filtering each time-series radar remote sensing image after correction by using the waveform correction-based method.

[0020] Further, step S21 comprises the following steps:

[0021] S21-1, finding relatively stable ground features in different phases and sensor time-series radar remote sensing images as pseudo-invariant feature points;

[0022] S21-2, based on the radiation value of the selected pseudo-invariant feature points, establishing a radiation conversion model from the source sensor to the target sensor.

[0023] S21-3, apply the established radiation conversion model to the entire source sensor time-series radar remote sensing image, so that its radiation characteristics are consistent with the target sensor time-series radar remote sensing image.

[0024] Further, the step S22 is specifically:

[0025] S22-1, calculate the cumulative probability distribution of the histogram of the radiation normalized time-series radar remote sensing image;

[0026] S22-2, according to the calculated cumulative probability distribution, find the lower limit gray value corresponding to the cumulative probability distribution value being the preset lower limit threshold value, and the upper limit gray value corresponding to the cumulative probability distribution value being the preset upper limit threshold value;

[0027] S22-3, truncate the gray value less than the lower limit gray value to the lower limit gray value, and truncate the gray value greater than the upper limit gray value to the upper limit gray value, and then recalculate the histogram of the time-series radar remote sensing image, to complete the correction.

[0028] Further, in the step S23, the filtering method is:

[0029] Taking each pixel in each time-series radar remote sensing image as the center, a neighborhood window is taken, and the pixels in the window are weighted and summed, and the expression is:

[0030]

[0031]

[0032] In the formula, indicates the gray value of the pixel in the window after weighted summation, indicates the gray value of the pixel, indicates the waveform correction filter function, indicates the standard deviation of the filter function, indicates the waveform correction coefficient, i, j indicates the horizontal and vertical coordinates of the pixel in the window, n indicates the total number of pixels in the window.

[0033] Further, in the step S3, the method for segmenting the flooded area and the non-flooded area is specifically:

[0034] S31, divide the preprocessed time-series radar remote sensing image into a plurality of local areas, and calculate the polarization ratio of each local area;

[0035] S32, in each local area, based on the backscattering intensity and the polarization ratio, the local Otsu method is used to determine the optimal segmentation threshold value of each local area.​

[0036] S33、According to the optimal segmentation threshold, the pixels in each local region are divided into the flooded area and the mask area.

[0037] Further, in the step S31, the pixels in each local region R ij , the polarization ratio is:

[0038]

[0039] In the formula, represents the backscattering intensity of HH polarization, represents the backscattering intensity of VV polarization;

[0040] In the step S32, when determining the optimal segmentation threshold of each local region, the objective function of the local Otsu method is to maximize the inter-class variance in the local region :

[0041]

[0042] In the formula, t represents the gray threshold value, and respectively represent the probabilities of the pixels with the gray value less than or equal to t and greater than t in the local region, and respectively represent the average gray values of the two types of pixels;

[0043] In the step S32, the gray threshold value that maximizes t is taken as the local optimal threshold .

[0044] Further, the step S4 includes the following sub-steps:

[0045] S41, based on the normal water area range, the flood evolution characteristic elements in the flooded range are quantitatively calculated, including the flooded area, the flooded speed and the water head position;

[0046] S42, based on the surface roughness and the digital elevation model in the flooded range, an improved Manning formula based on a simplified hydrodynamic model is constructed, and then the flood flow speed is calculated;

[0047] S43, according to the flood flow speed calculation, starting from the boundary pixel of the current flooded range, the expansion is carried out according to the flow direction and the terrain slope with a certain step time;

[0048] S44, for each extended pixel, when its elevation value is less than the average value of the elevation of the adjacent surrounding according to the inundation range area, the pixel is designated as a potential flood inundation area, and the potential impact area of the flood disaster is derived.

[0049] Further, in the step S41, the inundation area represents:

[0050]

[0051] The inundation speed represents:

[0052]

[0053] The water head position is the position coordinate of the boundary pixel of the most forward edge of the inundation range ;

[0054] In the formula, represents the inundation range at the moment, represents the inundation range The actual area corresponding to a single pixel in the inundation range, represents the inundation range The pixel in the inundation range, represents the inundation range at the moment;

[0055] The step S42 includes the following sub-steps:

[0056] S42-1, using the time sequence radar remote sensing image to obtain the surface roughness reflecting the surface resistance characteristics in the inundation area, and quantizing it as the roughness coefficient required by the hydrodynamic model;

[0057] S42-2, based on the digital elevation model of the inundation range and the surface roughness coefficient, constructing the improved Manning formula to simulate the surface water flow speed and direction, and then calculating the flood water flow speed:

[0058] In the formula, the improved Manning formula is:

[0059]

[0060] In the formula, represents the flood water flow speed, represents the roughness coefficient, represents the local water depth, represents the local terrain slope; wherein the flood water flow speed water flow direction The initial slope direction is determined by analyzing the elevation difference between the central pixel and its adjacent pixels.

[0061] The beneficial effects of the present application are:

[0062] (1) The present application constructs a flood monitoring and deduction method based on unmanned aerial vehicle time sequence radar remote sensing, which breaks through the problem that the large-scale flood evolution process is difficult to dynamically monitor;

[0063] (2) The present application constructs a radar remote sensing image preprocessing method of joint histogram statistical matching and waveform correction Gaussian filtering method, realizes radar value threshold optimization and image speckle noise;

[0064] (3) The present application adopts variance translation Otsu method and neighborhood window opening and closing method, realizes radar remote sensing high-resolution submerged range extraction;

[0065] (4) The present application quantitatively extracts flood evolution characteristic elements, combines digital elevation model and hydrological connectivity, realizes forward deduction of potential influence area of flood disaster. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 The present application provides a flood monitoring and deduction method based on unmanned aerial vehicle time sequence radar remote sensing. DETAILED DESCRIPTION

[0067] The specific embodiments of the present application are described below to facilitate those skilled in the art to understand the present application, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the present application defined and limited by the appended claims, and all inventions utilizing the concept of the present application are within the scope of protection.

[0068] The present application provides a flood monitoring and deduction method based on unmanned aerial vehicle time sequence radar remote sensing, as shown in Figure 1 The present application provides a flood monitoring and deduction method based on unmanned aerial vehicle time sequence radar remote sensing, as shown in

[0069] S1, collecting multi-source flood disaster high spatial resolution time sequence radar remote sensing data by unmanned aerial vehicle radar remote sensing, and processing it into a remote sensing data set with consistent space-time reference;

[0070] S2, preprocessing each time sequence radar remote sensing image in the remote sensing data set;

[0071] S3, segmenting the preprocessed time sequence radar remote sensing image into submerged area and non-submerged area, removing small water bodies in the submerged area, and extracting radar remote sensing high-resolution submerged range;

[0072] S4, quantitatively extracting flood evolution characteristic elements, and combining digital elevation model and improved Manning formula to deduce the potential influence area of flood disaster from the current submerged range.

[0073] In step S1 of the embodiment of the present application, the time-series radar remote sensing data is time-series radar remote sensing data obtained by networked observation of unmanned aerial vehicles and radar multi-band loads carried by the unmanned aerial vehicles. In addition, digital elevation model (DEM) data and vegetation coverage (NDVI) data of the region to be monitored are obtained by laser radar sensors carried by the unmanned aerial vehicles.

[0074] Specifically, networked observation of unmanned aerial vehicles is formed by original unmanned aerial vehicle ground stations and unmanned aerial vehicles deployed in flood emergency, and Ka-band, C-band or X-band radar loads and laser radar sensors are respectively carried on each unmanned aerial vehicle to perform high-frequency and multi-range data collection on the flood-affected region, so as to realize rapid acquisition of time-series radar remote sensing data of the flood-affected region and ensure that the spatio-temporal evolution process of the flood disaster can be fully reflected.

[0075] Due to the characteristics of the multi-sensor itself, the attitude change of the unmanned aerial vehicle platform and the influence of the earth curvature factor, geometric distortion will be generated in the unmanned aerial vehicle remote sensing image. Therefore, in the embodiment, the geometric distortion needs to be processed. In step S1 of the embodiment, the processing of the time-series radar remote sensing data includes:

[0076] The image coordinates of the time-series radar remote sensing data are converted to actual geographic coordinates in the geodetic coordinate system by establishing an affine transformation model representing the transformation relationship between the coordinates of the ground control points and the image coordinates of the time-series radar remote sensing data.

[0077] And the digital quantization value of the radar image in the time-series radar remote sensing data is converted into a radar backscatter coefficient.

[0078] Specifically, in the embodiment, it is assumed that the coordinates of the ground control points are (in the actual geodetic coordinate system), the corresponding coordinates on the image are The established affine transformation model is represented as:

[0079]

[0080] In the formula, the transformation parameters to be solved are obtained by a plurality of ground control points, so as to convert the image coordinates to actual geographic coordinates and correct the geometric distortion of the image.

[0081] In the embodiment, when the digital quantization value of the radar image is converted into a radar backscatter coefficient, for a multi-time-series satellite-borne radar sensor, the following calibration formula exists:

[0082]

[0083] In the formula, is a calibration constant defined by the sensor on the unmanned aerial vehicle; represents the digital quantization value of the image; is a parameter related to the radar beam illumination area, etc.

[0084] In this embodiment, the DN value can be converted into a backscattering coefficient with physical meaning through scaling, so as to compare different images.

[0085] The step S2 in the embodiment of the application comprises the following sub-steps:

[0086] S21, performing cross-sensor radiometric normalization processing on each time-series radar remote sensing image;

[0087] S22, correcting the histogram of the time-series radar remote sensing image subjected to the radiometric normalization processing by using the percentile intercept method;

[0088] S23, filtering each time-series radar remote sensing image subjected to the correction by using the waveform correction-based method.

[0089] In the step S21 of the embodiment, for the radiometric difference problem among multi-source time-series radar remote sensing data, a relative radiometric normalization method based on Pseudo-Invariant Features (PIFs) is used to improve the spatiotemporal consistency of the data, comprising the following sub-steps:

[0090] S21-1, finding a ground object with relatively stable radiometric characteristics on time-series radar remote sensing images of different time phases and sensors as a Pseudo-Invariant Feature;

[0091] S21-2, establishing a radiometric conversion model from a source sensor to a target sensor based on the radiometric value of the selected Pseudo-Invariant Feature;

[0092] S21-3, applying the established radiometric conversion model to the entire source sensor time-series radar remote sensing image to make its radiometric characteristics consistent with those of the target sensor time-series radar remote sensing image.

[0093] Specifically, in the step S21-1 of the embodiment, in the study area, a ground object with stable backscattering characteristics on time-series radar remote sensing images of different time phases and different sensors is selected as a Pseudo-Invariant Feature, and a standard can comprehensively consider the ground object type information and the time series variability of the backscattering coefficient, for example, for any pixel The time series variability coefficient of the backscattering coefficient at time can be expressed as:

[0094]

[0095] The selected The area below the set threshold is taken as a potential PIF area, and further screened in combination with feature type information.

[0096] In step S21-2 of this embodiment, based on the selected pseudo-invariant feature points , the backscattering coefficient values of the source sensor and the target sensor at the pseudo-invariant feature points are respectively represented as , and a nonlinear regression model is constructed. In this embodiment, a quadratic polynomial regression model is used to construct the radiation conversion model, which is represented as:

[0097]

[0098] wherein , , are undetermined regression coefficients, which are solved by the least square method; is a residual term.

[0099] In step S21-3 of this embodiment, the solved radiation conversion model is applied to each pixel of the source sensor time-series radar remote sensing image, to obtain the corrected backscattering coefficient value , which is represented as:

[0100]

[0101] Through the above method, the relative radiation normalization processing of the source sensor time-series radar remote sensing image to the target sensor time-series radar remote sensing image is realized.

[0102] Step S22 of this embodiment is specifically:

[0103] S22-1, calculating the cumulative probability distribution of the histogram of the radiation-normalized time-series radar remote sensing image ;

[0104] S22-2, according to the calculated cumulative probability distribution, finding the lower limit gray value corresponding to the cumulative probability distribution value being the preset lower limit threshold, and the upper limit gray value corresponding to the cumulative probability distribution value being the preset upper limit threshold;

[0105] Exemplarily, the above-mentioned preset lower limit threshold is represented as , and the preset upper limit threshold is represented as ;

[0106] S22-3, truncating the gray value less than the lower limit gray value to the lower limit gray value ​cut off the gray scale value greater than the upper limit gray scale value to the upper limit gray scale value and then re-calculate the histogram of the time-series radar remote sensing image to complete the correction.

[0107] In this embodiment, the influence of abnormal values at both ends of the image histogram on subsequent processing of the radar image is reduced through the above image histogram correction process.

[0108] In step S23 of this embodiment, the filtering method is:

[0109] Taking each pixel in the time-series radar remote sensing image as the center a neighborhood window, the pixels in the window are weighted and summed, and the expression is:

[0110]

[0111]

[0112] In the formula, denotes the gray scale value of the pixel in the window after weighted summation, denotes the gray scale value of the pixel, denotes the waveform correction filter function, denotes the standard deviation of the filter function, denotes the waveform correction coefficient, i, j denotes the horizontal and vertical coordinates of the pixel in the window, n denotes the total number of pixels in the window.

[0113] In this embodiment, through the above filtering process, the speckle noise in the time-series radar remote sensing image is removed, the waveform correction coefficient is set and adjusted to optimize the filtering effect, and the influence of house shadows and mountain shadows on water area extraction is removed to the greatest extent.

[0114] In step S3 of the embodiment of the application, a partition adaptive threshold segmentation method combined with polarization information is used to improve the accuracy of segmentation of the submerged area and the non-submerged area of the time-series radar remote sensing image, including the following steps:

[0115] S31, the preprocessed time-series radar remote sensing image is divided into a plurality of local areas, and the polarization ratio of each local area is calculated;

[0116] S32, in each local area, based on the backscattering intensity and the polarization ratio, a local Otsu method is used to determine the optimal segmentation threshold of each local area;

[0117] S33, according to the optimal segmentation threshold, the pixels in each local area are divided into a submerged area and a mask area.

[0118] In step S31 of the embodiment, the pre-processed time-series radar remote sensing image is divided into a plurality of non-overlapping or partially overlapping local regions R ij In each local region R ij , a polarization ratio :

[0119]

[0120] is calculated based on radar multi-polarization information (HH and VV polarization). , wherein represents the backscattering intensity of HH polarization, represents the backscattering intensity of VV polarization.

[0121] In step S32 of the embodiment, the backscattering intensity and the polarization ratio of the local region are combined to calculate the optimal segmentation threshold of the region by using the local Otsu method.

[0122] The objective function of the local Otsu method is to maximize the inter-class variance in the local region:

[0123]

[0124] , wherein t is the gray threshold value, and are the probabilities of the pixels with a gray value less than or equal to t and greater than t in the local region, respectively, and are the average gray values of the two types of pixels, respectively.

[0125] Based on the inter-class variance determined by the above method, by traversing all possible gray values in the local region, the t that maximizes is found as the local optimal threshold .

[0126] In step S33 of the embodiment, for each pixel R ij in the local region , the pixel is classified according to the comparison result of its backscattering intensity and the local optimal threshold .

[0127] Step S4 of the embodiment includes the following sub-steps:

[0128] S41, quantitatively calculate the flood evolution characteristic elements in the submerged range, including the submerged area, the submerged speed and the water head position, based on the normal water area range as the reference;

[0129] S42, based on the surface roughness and the digital elevation model in the submerged range, construct an improved Manning formula based on the simplified hydrodynamic model, and then calculate the flood flow speed;

[0130] S43, according to the flood flow speed, start from the boundary pixel of the current submerged range, expand according to the flow direction and the terrain slope, and take a certain step time;

[0131] S44, for each expanded pixel, when its elevation value is less than the average value of the elevation of the adjacent surrounding area in the submerged range, the pixel is defined as the potential flood submerged area, and then the potential influence area of the flood disaster is deduced.

[0132] In step S41 of the embodiment, the submerged area represents:

[0133]

[0134] The submerged speed represents:

[0135]

[0136] The water head position is the position coordinate of the boundary pixel of the most front of the submerged range ;

[0137] In the formula, represents the submerged range at the moment, represents the submerged range the actual area corresponding to a single pixel in the submerged range, represents the pixel in the submerged range represents the submerged range at the moment.

[0138] Step S42 in the embodiment includes the following steps:

[0139] S42-1, use the time sequence radar remote sensing image to obtain the surface roughness reflecting the surface resistance characteristics in the submerged area, and quantify it as the roughness coefficient required by the hydrodynamic model;

[0140] S42-2, based on the digital elevation model and the surface roughness coefficient of the submerged range, construct an improved Manning formula to simulate the surface flow speed and direction, and then calculate the flood flow speed:

[0141] wherein the improved Manning formula is:​

[0142]

[0143] In the formula, represents the flood flow velocity, represents the roughness coefficient, represents the local water depth (initially assumed to be a very small value or inferred according to the existing submerged range observed on the ground), represents the local terrain slope; wherein the flood flow velocity and the flow direction The initial slope direction is determined by analyzing the height difference between the central pixel and its adjacent pixels.

[0144] In step S42-1 of the embodiment, the normalized vegetation index and the ground radar coherence are extracted by using the time sequence radar remote sensing image, the surface roughness information of the study area is extracted, and it is quantified as the Manning roughness coefficient For example, an empirical relationship between the vegetation index and the Manning roughness coefficient can be established.

[0145] In steps S43 and S44 of the embodiment, for the boundary pixels of the current submerged range The priority direction of the flood expansion directly adopts the adjacent pixel direction indicated by the local flow velocity vector Of the current submerged range boundary pixel. Specifically, starting from the current submerged area boundary pixel, the expansion is carried out according to the flow direction and speed of the flood inundation and a certain step time For each expanded pixel If the elevation of the expanded pixel is The pixel is marked as a potential flood submerged area, otherwise it is considered as a non-submerged area, wherein The average elevation of the surrounding pixels is calculated according to the submerged area.

[0146] Based on the above process, the potential flood submerged area is delineated by calculating different time steps of flood evolution, so as to realize the forward deduction of the flood disaster image area, and the advance transfer and resettlement of the flood disaster bearing body are combined.

[0147] The principles and implementation modes of the present application are described in the specific embodiments, and the above embodiment description is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed, and the above description should not be understood as the limitation of the present application.

[0148] Those skilled in the art will appreciate that the embodiments described herein are presented for purposes of illustration and that the inventive principles are not limited to these particular embodiments. Other variations and modifications can be made to the embodiments without departing from the spirit and scope of the inventive principles.

Claims

1. A method for flood monitoring and prediction based on UAV time-series radar remote sensing, characterized in that, Includes the following steps: S1. Collect high spatial resolution temporal radar remote sensing data of multi-source flood disasters through UAV radar remote sensing, and process it into a remote sensing dataset with consistent spatiotemporal reference. S2. Preprocess each time-series radar remote sensing image in the remote sensing dataset; S3. The preprocessed time-series radar remote sensing image is divided into flooded and non-flooded areas, and small water bodies in the flooded area are removed to extract the high-resolution flooded range of the radar remote sensing. In step S3, the method for separating the flooded area from the non-flooded area is as follows: S31. Divide the preprocessed time-series radar remote sensing image into several local regions and calculate the polarization ratio of each local region. S32. Within each local region, the optimal segmentation threshold for each local region is determined using the local Otsu method based on the backscattering intensity and polarization ratio. S33. Based on the optimal segmentation threshold, the pixels in each local area are divided into flooded areas and masked areas; S4. Quantitatively extract the characteristic elements of flood evolution, and combine the digital elevation model and the improved Manning formula to extrapolate the potential impact area of ​​flood disasters on the current inundation range; Step S4 includes the following sub-steps: S41. Based on the normal water area, quantitatively calculate the flood evolution characteristics within the inundation area, including the inundation area, inundation rate, and water head position. S42. Based on the surface roughness and digital elevation model within the flooded area, an improved Manning formula based on a simplified hydrodynamic model is constructed to calculate the flood flow velocity. S43. Based on the flood flow velocity, starting from the boundary cell of the current inundation area, expand in steps according to the flow direction and terrain slope, with a certain time interval. S44. For each extended cell, when its elevation value is less than the average elevation of the surrounding area near the flooded area, the cell is designated as a potential flood inundation zone, thereby realizing the projection of the potential impact area of ​​flood disaster.

2. The flood monitoring and prediction method based on UAV time-series radar remote sensing according to claim 1, characterized in that, In step S1, the processing of time-series radar remote sensing data includes: By establishing an affine transformation model that characterizes the transformation relationship between ground control point coordinates and image coordinates of time-series radar remote sensing data, the image coordinates of time-series radar remote sensing data are transformed into actual geographic coordinates in the geodetic coordinate system. And converting the digital quantization values ​​of radar images in time-series radar remote sensing data into radar backscattering coefficients.

3. The flood monitoring and prediction method based on UAV time-series radar remote sensing according to claim 1, characterized in that, Step S2 includes the following sub-steps: S21. Perform cross-sensor radiometric normalization on radar remote sensing images of each time series. S22. The percentile truncation method is used to correct the histogram of the time-series radar remote sensing image after radiometric normalization. S23. A waveform correction-based method is used to filter each corrected time-series radar remote sensing image.

4. The flood monitoring and prediction method based on UAV time-series radar remote sensing according to claim 3, characterized in that, Step S21 includes the following sub-steps: S21-1. Find ground features with relatively stable radiometric characteristics in time-series radar remote sensing images of different time phases and sensors, and use them as pseudo-invariant feature points. S21-2. Based on the radiation values ​​of the selected pseudo-invariant feature points, establish a radiation conversion model from the source sensor to the target sensor; S21-3. Apply the established radiation conversion model to the entire source sensor time-series radar remote sensing image to make its radiation characteristics consistent with the target sensor time-series radar remote sensing image.

5. The flood monitoring and prediction method based on UAV time-series radar remote sensing according to claim 3, characterized in that, Step S22 specifically involves: S22-1. Calculate the cumulative probability distribution of the histogram of the time-series radar remote sensing image after radiometric normalization. S22-2. Based on the calculated cumulative probability distribution, find the lower limit gray value corresponding to the cumulative probability distribution value being a preset lower threshold, and the upper limit gray value corresponding to the cumulative probability distribution value being a preset upper threshold. S22-3. Truncate gray values ​​that are less than the lower limit gray value to the lower limit gray value, and truncate gray values ​​that are greater than the upper limit gray value to the upper limit gray value, and then recalculate the histogram of the time-series radar remote sensing image to complete the correction.

6. The flood monitoring and prediction method based on UAV time-series radar remote sensing according to claim 3, characterized in that, In step S23, the filtering method is as follows: Each pixel in each time-series radar remote sensing image Centered on a neighborhood window, we perform a weighted summation of the pixels within the window. The expression is as follows: In the formula, This represents the grayscale values ​​of the pixels within the window after weighted summation. Represents the grayscale value of a pixel. This represents a waveform correction filter function. The standard deviation of the filter function is represented by... Indicates the waveform correction factor. i, j Represents the x and y coordinates of the pixels within the window. n This indicates the total number of pixels in the window.

7. The flood monitoring and prediction method based on UAV time-series radar remote sensing according to claim 1, characterized in that, In step S31, each local region R ij polarization ratio for: In the formula, Indicates the backscattering intensity of HH polarization. This represents the backscattering intensity of VV polarization; In step S32, when determining the optimal segmentation threshold for each local region, the objective function of the local Otsu method is to maximize the inter-class variance within the local region. : In the formula, t Indicates the grayscale threshold. and These represent grayscale values ​​less than or equal to t And greater than t The probability of a pixel being located within a local region. and These represent the average grayscale values ​​of the two types of pixels, respectively. In step S32, the following will be made Maximum grayscale threshold t As a local optimal threshold .

8. The flood monitoring and prediction method based on UAV time-series radar remote sensing according to claim 1, characterized in that, In step S41, the flooded area express: The flooding rate express: The water head position is defined as the coordinates of the boundary cell at the forefront of the inundation area. ; In the formula, express The extent of submersion at any given moment. Indicates the flood range The actual area corresponding to a single pixel. Indicates the flood range The pixels inside, express The extent of submersion at any given moment; Step S42 includes the following sub-steps: S42-1. Use time-series radar remote sensing images to obtain the surface roughness reflecting the surface resistance characteristics within the flooded area, and quantify it into the roughness coefficient required for the hydrodynamic model. S42-2. Based on the digital elevation model of the inundation area and the surface roughness coefficient, an improved Manning formula is constructed to simulate the velocity and direction of surface water flow, and then the flood flow velocity is calculated: The improved Manning formula is as follows: In the formula, Indicates the speed of floodwater flow. Represents the roughness coefficient. Indicates local water depth. Indicates the local terrain slope; where floodwater flow velocity and direction are also included. The initial slope direction is determined by analyzing the elevation difference between the center pixel and its neighboring pixels.

Images