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CN-122016674-A - Multispectral remote sensing water quality monitoring method, multispectral remote sensing water quality monitoring system, electronic equipment and multispectral remote sensing water quality monitoring medium

CN122016674ACN 122016674 ACN122016674 ACN 122016674ACN-122016674-A

Abstract

The application provides a multispectral remote sensing water quality monitoring method, a multispectral remote sensing system, electronic equipment and a medium, wherein the multispectral remote sensing system is fixedly installed on the basis of a shore and acquires an original water body image of a target water area, and the multispectral remote sensing system is distributed at a preset angle with the horizontal surface of the shore; the method comprises the steps of carrying out image stitching and space correction based on original water body images to obtain corrected images, carrying out image distortion correction based on camera parameters and inclined image correction based on perspective transformation matrixes, carrying out water quality parameter inversion based on the corrected images to obtain water quality monitoring parameter images, carrying out abnormal water mass identification based on the water quality monitoring parameter images to obtain abnormal water mass parameter information, wherein the abnormal water mass parameter information at least comprises the area, the position and the drifting direction of an abnormal water mass, carrying out water quality monitoring based on the abnormal water mass parameter information to obtain a water quality state.

Inventors

  • WEI XIAODAO
  • CHEN SONGYU
  • LIAO TINGTING
  • ZHAO HAIYANG
  • SHEN DANNI

Assignees

  • 上海勘测设计研究院有限公司

Dates

Publication Date
20260512
Application Date
20260414

Claims (10)

  1. 1. A method for multi-spectral remote sensing water quality monitoring, comprising: Acquiring an original water body image of a target water area based on a multi-spectrum remote sensing device fixedly installed on a shore base, wherein the multi-spectrum remote sensing device is distributed at a preset angle with the horizontal surface of the shore base; performing image stitching and space correction based on each original water body image to obtain corrected images, wherein the space correction comprises image distortion correction based on camera parameters and inclined image correction based on a perspective transformation matrix; performing water quality parameter inversion based on the corrected image to obtain a water quality monitoring parameter image; The abnormal water mass parameter information at least comprises the area, the position and the drift direction of an abnormal water mass, wherein the abnormal water mass identification comprises image registration, pixel difference extraction and abnormal region extraction; and performing water quality monitoring based on the abnormal water group parameter information to obtain a water quality state, wherein the water quality state comprises a normal state and an abnormal pollution state.
  2. 2. The method of claim 1, wherein performing image stitching and spatial correction based on each of the original water images, obtaining corrected images comprises: performing image stitching based on each original water body image to obtain a stitched water body image; Performing image distortion correction by using camera parameters based on the spliced water body images to obtain a first corrected image, wherein the camera parameters comprise a camera internal parameter matrix, a distortion coefficient and an external parameter matrix; And correcting the inclined image based on the first corrected image to obtain a corrected image.
  3. 3. The method of claim 2, wherein performing oblique image correction based on the first corrected image, the obtaining corrected image comprising: Obtaining a perspective transformation matrix; and correcting the inclined image by utilizing the perspective transformation matrix based on the first corrected image to obtain a corrected image.
  4. 4. The method of claim 1, wherein performing water quality parameter inversion based on the corrected image to obtain a water quality monitoring parameter image comprises: and quantitatively inverting the corrected image by utilizing a water quality inversion algorithm model to obtain a water quality monitoring parameter image, wherein the water quality monitoring parameter image at least comprises a chlorophyll a concentration image, a potassium permanganate index image, a total nitrogen concentration image, a total phosphorus concentration image and a turbidity image.
  5. 5. The method for monitoring the water quality by multispectral remote sensing according to claim 4, wherein the water quality inversion algorithm model is a multiple regression model based on a selected key channel, and the expression is as follows: , wherein, Representing the ith raw water body image data, 、 、...、 Representing the reflectivity of the selected critical channel, 、 、 、...、 The regression coefficient is represented as a function of the regression coefficient, Representing error terms, the bands of the selected key channels include, but are not limited to 416.0nm, 449.4nm, 490.9nm, 554.7nm, 658.4nm, 676.6nm, 716.0nm, 839.7nm.
  6. 6. The method of claim 1, wherein performing abnormal water mass identification based on the water mass monitoring parameter image, and obtaining abnormal water mass parameter information comprises: Acquiring a water quality monitoring parameter image and a preset standard image at the current moment; Carrying out image registration on the water quality monitoring parameter image at the current moment and the preset standard image to obtain a registered image; Extracting pixel difference based on the registered images, extracting an abnormal region according to the extracted image pixel difference data, and obtaining the area and the position of an abnormal water mass; And determining the drift direction of the abnormal water mass according to the position change of the abnormal area at the continuous moment, wherein the position change of the abnormal area at the continuous moment is acquired by the original water body image at the continuous moment.
  7. 7. The method of claim 6, wherein extracting the abnormal region from the extracted image pixel difference data based on the registered image comprises: Performing gray level conversion based on the registered images to obtain a gray level image of the corrected image; performing gray level conversion based on a preset standard image to obtain a gray level image of the standard image; Acquiring image pixel difference data based on the gray level map of the corrected image and the gray level map of the standard image; Acquiring a comparison result based on the image pixel difference data and a preset threshold value; If the image pixel difference data is larger than the preset threshold value, the comparison result is that the corrected image has abnormal water clusters, and an abnormal area is extracted according to the image pixel difference data to obtain the area and the position of the abnormal water clusters; Otherwise, the comparison result shows that the corrected image does not have abnormal water clusters.
  8. 8. A multi-spectral remote sensing water quality monitoring system, comprising: The acquisition module is configured to acquire an original water body image of a target water area based on the shore-based fixedly installed multispectral remote sensing equipment, wherein the multispectral remote sensing equipment is arranged at a preset angle with the horizontal surface of the shore; The correction module is configured to carry out image stitching and space correction based on each original water body image, and obtain corrected images, wherein the space correction comprises image distortion correction based on camera parameters and inclined image correction based on a perspective transformation matrix; the water quality parameter inversion module is configured to perform water quality parameter inversion based on the corrected image to obtain a water quality monitoring parameter image; The abnormal water mass identification module is configured to perform abnormal water mass identification based on the water quality monitoring parameter image to acquire abnormal water mass parameter information, wherein the abnormal water mass parameter information at least comprises the area, the position and the drift direction of an abnormal water mass, and the abnormal water mass identification comprises image registration, pixel difference extraction and abnormal region extraction; the water quality monitoring module is configured to monitor water quality based on the abnormal water group parameter information, and acquire a water quality state, wherein the water quality state comprises a normal state and an abnormal pollution state.
  9. 9. An electronic device is characterized by comprising a processor and a memory, wherein, The memory is used for storing a computer program; The processor is configured to execute the computer program stored in the memory, so that the electronic device performs the multispectral remote sensing water quality monitoring method according to any one of claims 1 to 7.
  10. 10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the multi-spectral remote sensing water quality monitoring method of any of claims 1 to 7.

Description

Multispectral remote sensing water quality monitoring method, multispectral remote sensing water quality monitoring system, electronic equipment and multispectral remote sensing water quality monitoring medium Technical Field The application belongs to the technical field of water quality detection, and relates to a multispectral remote sensing water quality monitoring method, a multispectral remote sensing water quality monitoring system, electronic equipment and a multispectral remote sensing water quality monitoring medium. Background Along with the enhancement of social and economic development and environmental protection consciousness, the requirements for efficiently, accurately and comprehensively monitoring the water body environmental quality are increasingly urgent. Water quality monitoring is a key basis for assessing the health state of water, early warning pollution events and supporting management decisions. At present, the mainstream water quality monitoring technology mainly comprises three types of in-situ water quality sampling, real-time monitoring of a water quality station and unmanned aerial vehicle/satellite remote sensing monitoring, but each has obvious limitations: The in-situ water quality sampling has the advantages of strong artificial dependence, high cost, low space-time coverage capability, limited number of sampling points, sparse distribution, difficulty in reflecting the spatial heterogeneity of the water body, low monitoring frequency, usually discrete and periodic sampling, and incapability of capturing the rapid dynamic change of water quality parameters and sudden pollution events. Therefore, in-situ sampling is difficult to meet the modern water quality monitoring requirements of large-scale, high-frequency and real-time dynamics. The real-time monitoring of the water quality station can realize dynamic and continuous water quality monitoring, and is a relatively efficient monitoring means. But has a core problem in that the monitoring space is severely insufficient. The construction and maintenance costs of the water quality station are high, the number of stations is limited, the positions are fixed, only punctiform or extremely small-range data can be obtained, the acquisition capacity of the whole water quality condition of a wide water area (such as a large lake, a river and offshore), the space information such as a pollutant migration diffusion path and the like is very limited, and the real area monitoring cannot be realized. Unmanned aerial vehicle/satellite remote sensing monitoring, satellite remote sensing coverage is extremely wide, can realize global monitoring in theory. However, the application of the method is limited by a satellite reentry period (time resolution), the images of the same area can be obtained usually only for a plurality of days or even longer, the continuous high-frequency monitoring requirement is difficult to meet, the influence of atmospheric interference (particularly cloud cover) is serious, effective data are difficult to obtain in overcast and rainy or cloudy weather, the data continuity is poor, the time sequence is incomplete, the spatial resolution is limited, and the recognition capability for small-scale water bodies or detail features is insufficient. Compared with satellites, unmanned aerial vehicle remote sensing has higher flexibility, maneuverability and spatial resolution, and can be deployed as required to carry out area fine monitoring. But the application of the method is also limited by flight regulations, cannot be operated in a restricted flight area (such as the periphery of an airport, military facilities, densely populated areas, ecological protection areas and the like), is complex and time-consuming in flight approval process, particularly in a sensitive area, has limited single-flight endurance time, has a much smaller coverage area than a satellite, is also influenced by weather (such as strong wind and rainfall), is strictly limited by airspace management, is difficult to carry out long-term and frequent flight in the same area, and cannot realize uninterrupted continuous monitoring. In addition, the accuracy and universality of the remote sensing inversion model still need to be further improved. The current mainstream water quality monitoring technology has a bottleneck which is difficult to overcome: Aiming at the problems of image space distortion, inaccurate measurement and calculation of abnormal water mass area and position and insufficient abnormal recognition stability in a continuous monitoring scene of shore-based fixed multispectral remote sensing equipment under the oblique observation condition, satellites and unmanned aerial vehicles can be widely covered but have weak continuous monitoring capability (limited by periods, weather and airspace). The timeliness contradicts with cost and regulations, namely high-frequency and real-time monitoring (such as a water quality station) is high i