Satellite remote sensing image-based water regimen analysis method and system
By collecting infrared and visible remote sensing images, setting dynamic thresholds, dividing riverbed coordinates, obtaining riverbed and river channel areas, and conducting water situation analysis, the problems of low accuracy and efficiency of water situation analysis in the existing technology are solved, and efficient water situation analysis is achieved.
Patent Information
- Application Number
- CN202510609540.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-13
- Publication Date
- 2025-08-08
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In the prior art, the water situation analysis method has the problem of low analysis accuracy and low analysis efficiency.
By collecting infrared remote sensing images and visible remote sensing images of designated river basins, setting dynamic thresholds, using riverbed identification operators to divide riverbed coordinate information, obtain riverbed and river channel areas, and combining grayscale processing and water rise parameter correction, water situation analysis is achieved.
The accuracy and efficiency of water situation analysis are improved, and efficient water situation analysis of designated water basins is achieved.
Smart Images
Figure CN120451792A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data processing, and in particular to a water regime analysis method and system based on satellite remote sensing images. Background Art
[0002] Satellite remote sensing, a comprehensive technology system for observing the ground from space, is commonly used in various analytical processes, including water quality testing, fire analysis, and weather forecasting. However, existing water regime analysis methods often rely on statistical methods, resulting in low accuracy and efficiency.
[0003] Therefore, the water situation analysis method in the prior art has technical problems of low analysis accuracy and low analysis efficiency. Summary of the Invention
[0004] This application solves the technical problems of low analysis accuracy and low analysis efficiency in the water situation analysis methods in the prior art by providing a water situation analysis method and system based on satellite remote sensing images.
[0005] The present application provides a water regime analysis method based on satellite remote sensing images, the method is applied to a water regime analysis device based on satellite remote sensing images, the device including a remote sensing monitoring module, a riverbed analysis module, a river analysis module and a water regime analysis module, the method including: using the remote sensing detection module to collect infrared remote sensing images and visible remote sensing images of a designated watershed to be monitored and analyzed; setting a dynamic threshold for riverbed identification based on weather information when collecting remote sensing images; using a riverbed identification operator to segment the grayscale processed infrared remote sensing image to obtain multiple local infrared images, and according to the Dynamic threshold, identify multiple local infrared images, identify and obtain riverbed coordinate information set; based on the riverbed coordinate information set, crop the visible remote sensing image to obtain the river channel remote sensing image; based on the river channel remote sensing image, identify the riverbed area and river channel area; based on the riverbed area and river channel area, analyze and obtain the water rise parameters of the specified river basin; grayscale the visible remote sensing image, and extract the grayscale distribution information of the grayscale visible image, analyze and obtain the water rise trend information of the specified river basin, calibrate the water rise parameters, and obtain the water situation analysis results of the specified river basin.
[0006] The present application also provides a water situation analysis system based on satellite remote sensing images, the system includes a remote sensing monitoring module, a riverbed analysis module, a river analysis module and a water situation analysis module, the system includes: an image acquisition module for collecting infrared remote sensing images and visible remote sensing images of a designated watershed to be monitored and analyzed through a remote sensing detection module; a dynamic threshold acquisition module for setting a dynamic threshold for riverbed identification according to weather information when collecting remote sensing images; a riverbed coordinate acquisition module for segmenting the grayscale infrared remote sensing image using a riverbed identification operator to obtain multiple local infrared images, and performing segmentation on the multiple local infrared images according to the dynamic threshold. The system comprises a row recognition module, which is used to recognize and obtain a riverbed coordinate information set; an image cropping module, which is used to crop the visible remote sensing image according to the riverbed coordinate information set to obtain a river channel remote sensing image; an area acquisition module, which is used to recognize and obtain the riverbed area and the river channel area according to the river channel remote sensing image; a water rise parameter acquisition module, which is used to analyze and obtain the water rise parameters of the specified river basin according to the riverbed area and the river channel area; an analysis result acquisition module, which is used to grayscale the visible remote sensing image and extract the grayscale distribution information of the grayscale visible image, analyze and obtain the water rise trend information of the specified river basin, calibrate the water rise parameters, and obtain the water situation analysis result of the specified river basin.
[0007] The present application also provides an electronic device, comprising: a memory for storing executable instructions; The processor is used to implement the water situation analysis method based on satellite remote sensing images provided in this application when executing the executable instructions stored in the memory.
[0008] The present application provides a computer-readable storage medium storing a computer program. When the program is executed by a processor, the water regime analysis method based on satellite remote sensing images provided by the present application is implemented.
[0009] The water situation analysis method and system based on satellite remote sensing images proposed in this application is intended to collect infrared remote sensing images and visible remote sensing images of the designated river basin to be monitored and analyzed through a remote sensing detection module. According to the weather information when the remote sensing images are collected, a dynamic threshold for riverbed identification is set to identify and obtain a riverbed coordinate information set. According to the riverbed coordinate information set, a river channel remote sensing image is obtained. According to the river channel remote sensing image, the riverbed area and river channel area are identified. According to the riverbed area and river channel area, the water rise parameters of the designated river basin are analyzed and obtained. The remote sensing image is grayed, and the grayscale distribution information of the grayed visible image is extracted. The water rise trend information of the designated river basin is analyzed and the water rise parameters are corrected to obtain the water situation analysis results of the designated river basin. The analysis of the water situation of the designated river basin based on remote sensing images is realized, and the accuracy and efficiency of the water situation analysis of the designated river basin are improved. The technical problems of low analysis accuracy and low analysis efficiency in the water situation analysis methods in the existing technology are solved.
[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS
[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings of the embodiments of the present disclosure. Obviously, the drawings described below only relate to some embodiments of the present disclosure, and are not intended to limit the present disclosure.
[0012] Figure 1 A schematic diagram of a flow chart of a water regime analysis method based on satellite remote sensing images provided in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart of obtaining dynamic thresholds for a water regime analysis method based on satellite remote sensing images provided in an embodiment of the present application; Figure 3 A schematic diagram of the process of obtaining a riverbed coordinate information set using a water regime analysis method based on satellite remote sensing images provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of a system for a water regime analysis method based on satellite remote sensing images provided in an embodiment of the present application; Figure 5 A schematic diagram of the structure of the electronic equipment of the system for the water regime analysis method based on satellite remote sensing images provided by an embodiment of the present invention.
[0013] Explanation of the accompanying symbols: image acquisition module 11, dynamic threshold acquisition module 12, riverbed coordinate acquisition module 13, image cropping module 14, area acquisition module 15, water rise parameter acquisition module 16, analysis result acquisition module 17, processor 31, memory 32, input device 33, output device 34. DETAILED DESCRIPTION
[0014] Example 1
[0015] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.
[0016] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.
[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.
[0018] Although this application makes various references to certain modules in the systems according to embodiments of the application, any number of different modules may be used and run on the user terminal and / or server, the modules are illustrative only, and different aspects of the systems and methods may use different modules.
[0019] Flowcharts are used throughout this application to illustrate the operations performed by the systems of the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the various steps may be processed in reverse order or simultaneously, as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.
[0020] like Figure 1 As shown, an embodiment of the present application provides a water regime analysis method based on satellite remote sensing images. The method is applied to a water regime analysis device based on satellite remote sensing images. The device includes a remote sensing monitoring module, a riverbed analysis module, a river analysis module, and a water regime analysis module. The method includes: Through the remote sensing detection module, infrared remote sensing images and visible remote sensing images of the designated watershed to be monitored and analyzed are collected; According to the weather information when collecting remote sensing images, the dynamic threshold for riverbed identification is set; Using a riverbed recognition operator to segment the grayscale infrared remote sensing image to obtain multiple local infrared images, and then identifying the multiple local infrared images based on the dynamic threshold to obtain a riverbed coordinate information set; Satellite remote sensing refers to a comprehensive technical system that observes the ground from space. It is often used in various analytical processes, such as water quality testing, fire analysis, and weather forecasting. However, existing water regime analysis methods often use statistical analysis methods, resulting in low accuracy and efficiency in actual water regime analysis. To address these issues, a remote sensing detection module is used to collect infrared and visible remote sensing images of the designated watershed to be monitored and analyzed. Subsequently, a dynamic threshold for riverbed identification is set based on the weather information at the time the remote sensing images were collected. A riverbed identification operator is used, where the riverbed identification operator is a larger operator, such as an operator with 100*100 pixels, to facilitate initial and rapid image processing. The riverbed identification operator is used to segment the grayscale infrared remote sensing image to obtain multiple local infrared images. These local infrared images are then identified based on the dynamic threshold to obtain a set of riverbed coordinate information.
[0021] like Figure 2 As shown, the method provided in the embodiment of the present application also includes: Obtain historical weather information records and historical infrared remote sensing image records based on the historical remote sensing detection data of the designated watershed; Grayscale processing is performed on the infrared remote sensing images in the historical infrared remote sensing image records, and the grayscale value range of the riverbed under different weather information is extracted to obtain a historical dynamic threshold record; Use historical weather information records and historical dynamic threshold records to train the dynamic threshold classifier; The dynamic threshold is obtained by inputting the weather information into a dynamic threshold classifier.
[0022] Based on historical remote sensing data for a specified watershed, historical weather information records and historical infrared remote sensing image records are obtained. Subsequently, the infrared remote sensing images within these historical infrared remote sensing image records are grayscaled, and the grayscale value ranges of the riverbed under different weather conditions are extracted to obtain historical dynamic threshold records. These historical dynamic threshold records contain the grayscale value ranges of the riverbed under different weather conditions. Furthermore, a dynamic threshold classifier is trained using these historical weather information records and historical dynamic threshold records. Specifically, a classifier model is trained based on these historical weather information records and historical dynamic threshold records, enabling the dynamic threshold classifier to obtain corresponding dynamic thresholds based on the weather information. Finally, the dynamic threshold is obtained, which is obtained by inputting the weather information into the dynamic threshold classifier.
[0023] like Figure 3 As shown, the method provided in the embodiment of the present application also includes: The riverbed recognition operator is used to segment the grayscale infrared remote sensing image to obtain multiple local infrared images. According to the dynamic threshold, pixel points in the plurality of grayscale local infrared images are discriminated, and pixel points meeting the dynamic threshold are extracted to obtain a riverbed point set; Based on the coordinates of the riverbed point set in the infrared remote sensing image, the riverbed coordinate information set is obtained.
[0024] A riverbed recognition operator is used to segment the grayscale infrared remote sensing image to obtain multiple local infrared images. Subsequently, pixels within the grayscale local infrared images are identified based on the dynamic threshold value obtained from weather information. Pixels that meet the dynamic threshold value are extracted to obtain a riverbed point set. The riverbed coordinate information set is obtained based on the coordinates of the riverbed point set within the infrared remote sensing image.
[0025] According to the riverbed coordinate information set, the visible remote sensing image is cropped to obtain a river channel remote sensing image; Identify the riverbed area and river channel area based on the river channel remote sensing image; Analyze and obtain water rise parameters of a designated watershed based on the riverbed area and river channel area; The visible remote sensing image is gray-scaled, and grayscale distribution information of the gray-scaled visible image is extracted to obtain water rise trend information of the specified watershed through analysis, and the water rise parameters are corrected to obtain water situation analysis results of the specified watershed.
[0026] According to the acquired riverbed coordinate information set, the visible remote sensing image is cropped to obtain a river channel remote sensing image, that is, the visible remote sensing image is cropped according to the acquired riverbed coordinate information set to obtain a cropped river channel remote sensing image. Subsequently, the river channel remote sensing image is identified to obtain the riverbed area and the river channel area. Further, according to the riverbed area and the river channel area, the water rise parameters of the specified river basin are analyzed and obtained on the basis of the initial riverbed area and the initial river channel area. Finally, the visible remote sensing image is grayed, and the grayscale distribution information of the grayed visible image is extracted. Based on the water rise trend comparison table, the water rise trend information of the specified river basin is analyzed and obtained, and the water rise parameters are corrected to obtain the water situation analysis results of the specified river basin. The analysis of the water situation of the specified river basin based on remote sensing images is realized, and the accuracy and efficiency of the water situation analysis of the specified river basin are improved.
[0027] The method provided in the embodiment of the present application also includes: Using a river identification operator to segment the grayscale processed river channel remote sensing image to obtain multiple local river channel images, the river identification operator is smaller than the riverbed identification operator; Calculate the grayscale difference between the minimum grayscale value and the maximum grayscale value in multiple grayscale local river images, and determine whether the grayscale difference is greater than the grayscale difference threshold; If yes, the local river channel image is marked as a river channel image to be identified, and multiple river channel images to be identified are obtained; If not, the average gray value in the local river channel image is calculated, and it is determined whether the average gray value meets the riverbed average gray value threshold of the specified basin or the river channel average gray value threshold, and multiple river channel area images and multiple riverbed area images are obtained; Identify the multiple river channel images to be identified to obtain multiple identification result sets, each identification result set including a riverbed area and a river channel area; The riverbed area and the river channel area are calculated based on the multiple recognition result sets, the multiple river channel area images and the multiple riverbed area images.
[0028] A river identification operator is used to segment the grayscale-processed river channel remote sensing image to obtain multiple local river channel images. The river identification operator is smaller than the riverbed identification operator. The river identification operator is used to segment the river channel remote sensing image. For example, if the riverbed identification operator is a 100*100 operator, the corresponding river identification operator can be a 10*10 operator. This allows for fine segmentation of the river channel remote sensing image, facilitating subsequent identification processing. Subsequently, due to the significant grayscale difference between the river and the riverbed, the grayscale difference between the minimum and maximum grayscale values within the multiple grayscaled local river channel images is calculated to determine whether the grayscale difference exceeds a grayscale difference threshold. The grayscale difference threshold is a pre-set maximum grayscale difference threshold. If the grayscale difference exceeds the threshold, it is assumed that both the riverbed and the river channel exist. If the grayscale difference does not exceed the threshold, it indicates that the image is entirely riverbed or entirely river channel. If the grayscale difference exceeds the threshold, the local river channel image is marked as a river channel image to be identified, resulting in multiple river channel images to be identified. When the grayscale difference is less than or equal to the grayscale difference threshold, the average grayscale value in the local river channel image is calculated, and it is judged whether the average grayscale value meets the riverbed average grayscale threshold of the specified basin or meets the river channel average grayscale threshold. Multiple river channel area images and multiple riverbed area images are marked. Due to the difference in grayscale between the river channel and the riverbed, the average grayscale threshold is pre-set to judge the average grayscale of different images, thereby realizing the distinction between the river channel and the riverbed. Among them, the average grayscale threshold is a pre-set grayscale value used to distinguish the river channel from the riverbed.
[0029] Furthermore, the plurality of river channel images to be identified are identified to obtain a plurality of identification result sets, each of which includes a riverbed area and a river channel area. Finally, based on the plurality of identification result sets, the plurality of river channel area images, and the plurality of riverbed area images, the riverbed areas and river channel areas of the plurality of identification result sets, the plurality of river channel area images, and the plurality of riverbed area images are pixel-accumulated to obtain the riverbed area and the river channel area based on the relationship between the pixels and the actual ratio.
[0030] The method provided in the embodiment of the present application also includes: Based on the historical remote sensing detection data of the designated watershed, the image records of the river channel to be identified are processed and recorded, and the image segmentation and identification of the river channel area and riverbed area are performed to obtain the sample identification result records; Constructing an encoder and decoder to form a river image recognition path, wherein the encoder and decoder are constructed based on semantic segmentation; Using sample river channel image records and sample recognition result records, the encoder and decoder are trained until convergence, and embedded in the river analysis module; A plurality of recognition result sets are obtained, where the plurality of recognition result sets are obtained by inputting a plurality of river channel images to be recognized into the river channel image recognition path.
[0031] Based on the historical data of remote sensing detection of the specified watershed, the sample river channel image records to be identified are processed and the image segmentation and identification of the river channel area and riverbed area are performed by manual identification to obtain the sample identification result records. Subsequently, an encoder and a decoder are constructed to form a river channel image recognition path. The encoder and the decoder are constructed based on semantic segmentation. The semantic segmentation model is trained using the obtained sample river channel image records to be identified and the sample identification result records. The training is completed when the model converges, that is, the recognition result output by the trained semantic segmentation model meets the preset accuracy, and the model is embedded in the river analysis module. Finally, the multiple river channel images to be identified are input into the river channel image recognition path to obtain multiple recognition result sets, and the multiple recognition result sets all contain the identified river channel area and riverbed area.
[0032] The method provided in the embodiment of the present application also includes: Obtaining the initial riverbed area and initial river channel area of the designated watershed; Retrieve sample riverbed area records, sample river channel area records, and sample water level parameter records from multiple flooding events in the designated river basin's history; According to the sample riverbed area records and sample river channel area records, with the initial riverbed area and initial river channel area as the benchmark, calculate and obtain the river channel area change coefficient record; Using the river area change coefficient record and the sample water rise parameter record, a water rise parameter analysis path is constructed based on a decision tree and embedded in the water regime analysis module; The riverbed area, river channel area, initial riverbed area and initial river channel area are combined to calculate and obtain the river channel area variation coefficient, and the water rise parameter is obtained by decision.
[0033] Obtain the initial riverbed area and initial channel area of the specified watershed. Retrieve the sample riverbed area records, sample channel area records and sample water rise parameter records for multiple floods in the specified watershed in history. Based on the sample riverbed area records and sample channel area records, and taking the initial riverbed area and initial channel area as the benchmark, calculate and obtain the channel area change coefficient record, that is, calculate the area difference between the corresponding riverbed area record and channel area record in the water rise parameter record through the initial riverbed area and the initial channel area, add the absolute value of the area difference data to obtain the difference data, and then obtain the channel area change coefficient. Subsequently, using the channel area change coefficient record and the sample water rise parameter record, a water rise parameter analysis path is constructed based on a decision tree, and the water situation analysis module is embedded. The decision tree is used to obtain the corresponding sample water rise parameter data according to the channel area change coefficient. Finally, the channel area change coefficient is calculated and obtained by the obtained riverbed area, channel area, initial riverbed area and initial channel area, and the water rise parameter is obtained by decision.
[0034] The method provided in the embodiment of the present application also includes: Perform grayscale processing on visible remote sensing images to obtain grayscale visible images; The grayscale values of all pixels in the visible grayscale image are counted, and the grayscale values that appear are weighted according to the number of times each grayscale value appears to obtain grayscale distribution information; According to the remote sensing detection data of the designated watershed, the sample gray distribution information set and the sample rainfall flow information set are processed and obtained; Based on the sample rainfall flow information set, the sample water rise trend information set is evaluated and obtained; Using the sample grayscale distribution information set and the sample water rise trend information set, constructing a water rise trend comparison table; The grayscale distribution information is used for mapping and matching to obtain the water rise trend information.
[0035] The visible remote sensing image is grayscaled to obtain a grayscale visible image. The grayscale values of all pixels within the visible grayscale image are counted, and the grayscale values are weighted based on the number of occurrences of each grayscale value to obtain grayscale distribution information. Specifically, the weight of each grayscale value is obtained, and the grayscale values are weighted to obtain grayscale distribution information. Based on historical remote sensing data from a specified watershed, a sample grayscale distribution information set and a sample rainfall flow information set are obtained. Specifically, the grayscale distribution information of the historical remote sensing data and the sample rainfall flow information are obtained to form the sample grayscale distribution information set and the sample rainfall flow information set. Based on the sample rainfall flow information set, a sample water rise trend information set is obtained through professional evaluation. Furthermore, a water rise trend comparison table is constructed using the sample grayscale distribution information set and the sample water rise trend information set, which represents the correspondence between the grayscale distribution information and the sample water rise trend. Finally, the water rise trend information is obtained through mapping and matching based on the calculated grayscale distribution information.
[0036] The technical solution provided by the embodiment of the present invention uses a remote sensing detection module to collect infrared remote sensing images and visible remote sensing images of a designated river basin to be monitored and analyzed. According to the weather information when the remote sensing images are collected, a dynamic threshold for riverbed identification is set to identify and obtain a riverbed coordinate information set. According to the riverbed coordinate information set, the visible remote sensing image is cropped to obtain a river channel remote sensing image. According to the river channel remote sensing image, the riverbed area and river channel area are identified. According to the riverbed area and river channel area, the water rise parameters of the designated river basin are analyzed and obtained. The visible remote sensing image is grayed, and the grayscale distribution information of the grayed visible image is extracted. The water rise trend information of the designated river basin is analyzed and obtained. The water rise parameters are corrected to obtain the water situation analysis results of the designated river basin. The water situation analysis of the designated river basin based on remote sensing images is realized, and the accuracy and efficiency of the water situation analysis of the designated river basin are improved. The technical problems of low analysis accuracy and low analysis efficiency in the water situation analysis methods in the prior art are solved.
[0037] Example 2
[0038] Based on the same inventive concept as the water regime analysis method based on satellite remote sensing images in the aforementioned embodiment, the present invention also provides a system for water regime analysis method based on satellite remote sensing images. The system can be implemented in hardware and / or software and can generally be integrated into electronic devices to execute the method provided by any embodiment of the present invention. Figure 4 As shown, the system includes a remote sensing monitoring module, a riverbed analysis module, a river analysis module and a water condition analysis module. The system includes: The image acquisition module 11 is used to collect infrared remote sensing images and visible remote sensing images of the designated watershed to be monitored and analyzed through the remote sensing detection module; A dynamic threshold acquisition module 12 is used to set a dynamic threshold for riverbed identification based on weather information when remote sensing images are collected; The riverbed coordinate acquisition module 13 is configured to segment the grayscale infrared remote sensing image using a riverbed recognition operator to obtain a plurality of local infrared images, and recognize the plurality of local infrared images based on the dynamic threshold value to obtain a riverbed coordinate information set; An image cropping module 14 is configured to crop the visible remote sensing image according to the riverbed coordinate information set to obtain a river channel remote sensing image; An area acquisition module 15 is used to identify the riverbed area and the river channel area based on the river channel remote sensing image; A water rise parameter acquisition module 16 is used to analyze and acquire water rise parameters of a specified river basin based on the riverbed area and the river channel area; The analysis result acquisition module 17 is used to grayscale the visible remote sensing image, extract the grayscale distribution information of the grayscale visible image, analyze and obtain the water rise trend information of the specified river basin, correct the water rise parameters, and obtain the water situation analysis results of the specified river basin.
[0039] Furthermore, the dynamic threshold acquisition module 12 is further configured to: Obtain historical weather information records and historical infrared remote sensing image records based on the historical remote sensing detection data of the designated watershed; Grayscale processing is performed on the infrared remote sensing images in the historical infrared remote sensing image records, and the grayscale value range of the riverbed under different weather information is extracted to obtain a historical dynamic threshold record; Use historical weather information records and historical dynamic threshold records to train the dynamic threshold classifier; The dynamic threshold is obtained by inputting the weather information into a dynamic threshold classifier.
[0040] Furthermore, the riverbed coordinate acquisition module 13 is also used for: The riverbed recognition operator is used to segment the grayscale infrared remote sensing image to obtain multiple local infrared images. According to the dynamic threshold, pixel points in the plurality of grayscale local infrared images are discriminated, and pixel points meeting the dynamic threshold are extracted to obtain a riverbed point set; Based on the coordinates of the riverbed point set in the infrared remote sensing image, the riverbed coordinate information set is obtained.
[0041] Furthermore, the area acquisition module 15 is further configured to: Using a river identification operator to segment the grayscale processed river channel remote sensing image to obtain multiple local river channel images, the river identification operator is smaller than the riverbed identification operator; Calculate the grayscale difference between the minimum grayscale value and the maximum grayscale value in multiple grayscale local river images, and determine whether the grayscale difference is greater than the grayscale difference threshold; If yes, the local river channel image is marked as a river channel image to be identified, and multiple river channel images to be identified are obtained; If not, the average gray value in the local river channel image is calculated, and it is determined whether the average gray value meets the riverbed average gray value threshold of the specified basin or the river channel average gray value threshold, and multiple river channel area images and multiple riverbed area images are obtained; Identify the multiple river channel images to be identified to obtain multiple identification result sets, each identification result set including a riverbed area and a river channel area; The riverbed area and the river channel area are calculated based on the multiple recognition result sets, the multiple river channel area images and the multiple riverbed area images.
[0042] Furthermore, the area acquisition module 15 is further configured to: Based on the historical remote sensing detection data of the designated watershed, the image records of the river channel to be identified are processed and recorded, and the image segmentation and identification of the river channel area and riverbed area are performed to obtain the sample identification result records; Constructing an encoder and decoder to form a river image recognition path, wherein the encoder and decoder are constructed based on semantic segmentation; Using sample river channel image records and sample recognition result records, the encoder and decoder are trained until convergence, and embedded in the river analysis module; A plurality of recognition result sets are obtained, where the plurality of recognition result sets are obtained by inputting a plurality of river channel images to be recognized into the river channel image recognition path.
[0043] Furthermore, the water rise parameter acquisition module 16 is further configured to: Obtaining the initial riverbed area and initial river channel area of the designated watershed; Retrieve sample riverbed area records, sample river channel area records, and sample water level parameter records from multiple flooding events in the designated river basin's history; According to the sample riverbed area records and sample river channel area records, with the initial riverbed area and initial river channel area as the benchmark, calculate and obtain the river channel area change coefficient record; Using the river area change coefficient record and the sample water rise parameter record, a water rise parameter analysis path is constructed based on a decision tree and embedded in the water regime analysis module; The riverbed area, river channel area, initial riverbed area and initial river channel area are combined to calculate and obtain the river channel area variation coefficient, and the water rise parameter is obtained by decision.
[0044] Furthermore, the analysis result acquisition module 17 is further configured to: Perform grayscale processing on visible remote sensing images to obtain grayscale visible images; The grayscale values of all pixels in the visible grayscale image are counted, and the grayscale values that appear are weighted according to the number of times each grayscale value appears to obtain grayscale distribution information; According to the remote sensing detection data of the designated watershed, the sample gray distribution information set and the sample rainfall flow information set are processed and obtained; Based on the sample rainfall flow information set, the sample water rise trend information set is evaluated and obtained; Using the sample grayscale distribution information set and the sample water rise trend information set, constructing a water rise trend comparison table; The grayscale distribution information is used for mapping and matching to obtain the water rise trend information.
[0045] The various units and modules included are divided only according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.
[0046] Example 3
[0047] Figure 5 This is a structural diagram of an electronic device provided in accordance with a third embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 5 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 5 As shown, the electronic device includes a processor 31, a memory 32, an input device 33 and an output device 34; the number of processors 31 in the electronic device can be one or more. Figure 5 Taking a processor 31 as an example, the processor 31, memory 32, input device 33 and output device 34 in the electronic device can be connected through a bus or other means. Figure 5 The bus connection is taken as an example.
[0048] Memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the water regime analysis method based on satellite remote sensing imagery in the embodiments of the present invention. Processor 31 executes the software programs, instructions, and modules stored in memory 32 to perform various computer functions and data processing, thereby implementing the aforementioned water regime analysis method based on satellite remote sensing imagery.
[0049] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.
Claims
1. A water regime analysis method based on satellite remote sensing images, wherein the method is applied to a water regime analysis device based on satellite remote sensing images, the device comprising a remote sensing monitoring module, a riverbed analysis module, a river analysis module, and a water regime analysis module, and wherein: The method comprises: Through the remote sensing detection module, infrared remote sensing images and visible remote sensing images of the designated watershed to be monitored and analyzed are collected; According to the weather information when collecting remote sensing images, the dynamic threshold for riverbed identification is set; Using a riverbed recognition operator to segment the grayscale infrared remote sensing image to obtain multiple local infrared images, and then identifying the multiple local infrared images based on the dynamic threshold to obtain a riverbed coordinate information set; According to the riverbed coordinate information set, the visible remote sensing image is cropped to obtain a river channel remote sensing image; Identify the riverbed area and river channel area based on the river channel remote sensing image; Analyze and obtain water rise parameters of a designated watershed based on the riverbed area and river channel area; The visible remote sensing image is gray-scaled, and grayscale distribution information of the gray-scaled visible image is extracted to obtain water rise trend information of the specified watershed through analysis, and the water rise parameters are corrected to obtain water situation analysis results of the specified watershed.
2. The method according to claim 1, characterized in that The method comprises: Obtain historical weather information records and historical infrared remote sensing image records based on the historical remote sensing detection data of the designated watershed; Grayscale processing is performed on the infrared remote sensing images in the historical infrared remote sensing image records, and the grayscale value range of the riverbed under different weather information is extracted to obtain a historical dynamic threshold record; Use historical weather information records and historical dynamic threshold records to train the dynamic threshold classifier; The dynamic threshold is obtained by inputting the weather information into a dynamic threshold classifier.
3. The method according to claim 1, characterized in that The grayscale infrared remote sensing image is segmented using a riverbed recognition operator to obtain multiple local infrared images. The multiple local infrared images are then identified based on the dynamic threshold to obtain a riverbed coordinate information set, including: The riverbed recognition operator is used to segment the grayscale infrared remote sensing image to obtain multiple local infrared images. According to the dynamic threshold, pixel points in the plurality of grayscale local infrared images are discriminated, and pixel points meeting the dynamic threshold are extracted to obtain a riverbed point set; Based on the coordinates of the riverbed point set in the infrared remote sensing image, the riverbed coordinate information set is obtained.
4. The method according to claim 1, wherein The method comprises: Using a river identification operator to segment the grayscale processed river channel remote sensing image to obtain multiple local river channel images, the river identification operator is smaller than the riverbed identification operator; Calculate the grayscale difference between the minimum grayscale value and the maximum grayscale value in multiple grayscale local river images, and determine whether the grayscale difference is greater than the grayscale difference threshold; If yes, the local river channel image is marked as a river channel image to be identified, and multiple river channel images to be identified are obtained; If not, the average gray value in the local river channel image is calculated, and it is determined whether the average gray value meets the riverbed average gray value threshold of the specified basin or the river channel average gray value threshold, and multiple river channel area images and multiple riverbed area images are obtained; Identify the multiple river channel images to be identified to obtain multiple identification result sets, each identification result set including a riverbed area and a river channel area; The riverbed area and the river channel area are calculated based on the multiple recognition result sets, the multiple river channel area images and the multiple riverbed area images.
5. The method according to claim 4, characterized in that The method comprises: Based on the historical remote sensing detection data of the designated watershed, the image records of the river channel to be identified are processed and recorded, and the image segmentation and identification of the river channel area and riverbed area are performed to obtain the sample identification result records; Constructing an encoder and decoder to form a river image recognition path, wherein the encoder and decoder are constructed based on semantic segmentation; Using sample river channel image records and sample recognition result records, the encoder and decoder are trained until convergence, and embedded in the river analysis module; A plurality of recognition result sets are obtained, where the plurality of recognition result sets are obtained by inputting a plurality of river channel images to be recognized into the river channel image recognition path.
6. The method according to claim 1, characterized in that The method comprises: Obtaining the initial riverbed area and initial river channel area of the designated watershed; Retrieve sample riverbed area records, sample river channel area records, and sample water level parameter records from multiple flooding events in the designated river basin's history; According to the sample riverbed area records and sample river channel area records, with the initial riverbed area and initial river channel area as the benchmark, calculate and obtain the river channel area change coefficient record; Using the river area change coefficient record and the sample water rise parameter record, a water rise parameter analysis path is constructed based on a decision tree and embedded in the water regime analysis module; The riverbed area, river channel area, initial riverbed area and initial river channel area are combined to calculate and obtain the river channel area variation coefficient, and the water rise parameter is obtained by decision.
7. The method according to claim 1, characterized in that The method comprises: Perform grayscale processing on visible remote sensing images to obtain grayscale visible images; The grayscale values of all pixels in the visible grayscale image are counted, and the grayscale values that appear are weighted according to the number of times each grayscale value appears to obtain grayscale distribution information; According to the remote sensing detection data of the designated watershed, the sample gray distribution information set and the sample rainfall flow information set are processed and obtained; Based on the sample rainfall flow information set, the sample water rise trend information set is evaluated and obtained; Using the sample grayscale distribution information set and the sample water rise trend information set, constructing a water rise trend comparison table; The grayscale distribution information is used for mapping and matching to obtain the water rise trend information.
8. A water regime analysis system based on satellite remote sensing images, comprising a remote sensing monitoring module, a riverbed analysis module, a river analysis module, and a water regime analysis module, characterized in that: The system comprises: An image acquisition module is used to collect infrared remote sensing images and visible remote sensing images of the designated watershed to be monitored and analyzed through the remote sensing detection module; The dynamic threshold acquisition module is used to set the dynamic threshold for riverbed identification based on the weather information when the remote sensing image is collected; A riverbed coordinate acquisition module is used to segment the grayscale infrared remote sensing image using a riverbed recognition operator to obtain multiple local infrared images, and to recognize the multiple local infrared images based on the dynamic threshold to obtain a riverbed coordinate information set; An image cropping module is used to crop the visible remote sensing image according to the riverbed coordinate information set to obtain a river channel remote sensing image; An area acquisition module is used to identify the riverbed area and the river channel area based on the river channel remote sensing image; A water rise parameter acquisition module, configured to analyze and acquire water rise parameters of a specified watershed based on the riverbed area and the river channel area; The analysis result acquisition module is used to grayscale the visible remote sensing image, extract the grayscale distribution information of the grayscale visible image, analyze and obtain the water rise trend information of the specified river basin, correct the water rise parameters, and obtain the water situation analysis results of the specified river basin.
9. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable instructions; The processor is configured to implement the water regime analysis method based on satellite remote sensing images as described in any one of claims 1 to 7 when executing the executable instructions stored in the memory.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the water regime analysis method based on satellite remote sensing images as described in any one of claims 1 to 7 is implemented.