CN106355166B - Dust-haze diffusion path drawing and source determining method based on monitoring video and remote sensing image - Google Patents
Dust-haze diffusion path drawing and source determining method based on monitoring video and remote sensing image Download PDFInfo
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- CN106355166B CN106355166B CN201610893523.2A CN201610893523A CN106355166B CN 106355166 B CN106355166 B CN 106355166B CN 201610893523 A CN201610893523 A CN 201610893523A CN 106355166 B CN106355166 B CN 106355166B
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Abstract
The invention relates to a gray-haze diffusion path drawing and source determining method based on a monitoring video and a remote sensing image. The method can be used for timely positioning the direct source of the dust-haze generation in the specific area in real time; the invention has the advantages of high identification precision and high efficiency.
Description
The technical field is as follows:
the invention belongs to the field of computer image processing technology and atmospheric pollutant monitoring, and particularly relates to a method for tracing direct sources of dust and haze by dividing areas by grids and analyzing pollution index change trends reflected by monitoring video data and remote sensing image data in the grids.
Background art:
at present, the source of the atmospheric composite pollution is complex and is in rapid change, and the current situation of improving the air quality is still severe. At present, policies such as 'production limit and production halt, shutdown limit' and the like which are macroscopically adopted are non-permanent and temporary 'one-time' measures, the problems of serious enterprise stealing, monitoring equipment damage, malicious monitoring data forgery and the like cannot be solved, the enthusiasm and economic benefits of law-keeping enterprises are greatly damaged, and the 'normalization' of air quality improvement cannot be guaranteed.
The invention patent 201310141896.0 discloses a haze monitoring method based on computer vision, which provides a monitoring result for haze based on the calculation of the visual characteristics of a target object and the comparison of the target object with sample images under different haze conditions. And comparing the images through colors, shapes, textures and characteristic vectors representing the difference between the far and near objects to obtain the gray haze level.
Monitoring videos are mainly used for traffic and safety monitoring at present, and applications related to dust and haze are mainly focused on defogging of video images. In document 1, the image haze levels are classified into large fog, small fog, and no fog by analyzing the high-speed monitored dust-haze images.
Currently, the atmospheric pollutant diffusion mode is widely used for simulating and predicting the diffusion distribution of pollutants and evaluating the quality of the atmospheric environment (reference 2). The atmospheric pollutant diffusion mode combines pollutant concentration and meteorological data to quantitatively analyze the transport and diffusion characteristics of pollutants in the atmosphere. Initially, the theoretical core of research in the model was gaussian diffusion theory, and the range of application was small scale. With the gradual deepening of research and the development of computers, numerical calculation is started to be carried out by using the computers, and the application range of the mode is expanded to a medium scale and a large scale. At present, numerical calculation has become a mainstream method of research, and the research range is gradually expanded. But the method is limited by complicated terrain conditions under small scale and scarcity of pollution monitoring equipment, and can not effectively monitor the dust-haze generating source and determine the responsibility subject in real time.
The remotely sensed image may be used to determine the source of the contaminant for the region. And 3, the remote sensing image can be used for tracing the haze source of a certain area to obtain the pollutant source judgment of the area level.
Document 1: summary of the inventionthe was created in summer, expressway network operation monitoring several key technical studies [ D ]. south china university, 2013.
Document 2: application study of the model of atmospheric pollutants diffusion in Zhaoyuan, Zhang Yan, reviews [ J ] environmental pollution and control 2007(05)
Document 3: sheep and the like are worn, remote sensing-based Shanghai region haze monitoring research [ J ]. mapping engineering 2015(12)
The invention content is as follows:
according to the method, only three independent parts, namely, evaluation of the dust-haze grade, monitoring of local dust-haze by using a monitoring video and simulation of a pollutant diffusion mode by using numerical calculation are used for respectively explaining how to monitor the dust-haze, but the fog and the haze cannot be distinguished, the whole large-density monitoring is not dynamically linked, the dust-haze grade is not monitored along with the change of time, a dust-haze diffusion path is not drawn, and the source place where the dust-haze is directly generated cannot be determined. Meanwhile, the numerical simulation pollutant diffusion is greatly different from the actual pollutant diffusion. And only dynamic and global trend analysis is carried out on the dust-haze grade change, the dust-haze diffusion path can be drawn, and the dust-haze emission main body is further determined.
The invention provides a method, which is characterized in that a grid area is divided, an ash-haze image sample library is constructed, video image haze grade change monitoring in a grid is adopted, multi-temporal dynamic analysis and remote sensing image global analysis are combined, and an ash-haze diffusion path and distribution are drawn, so that an ash-haze direct source and a responsibility main body in a specific grid are positioned. The regional gridding is a precondition, the gray-haze level division and multi-temporal dynamic analysis of the video image are key, and the accuracy of the correlation analysis of related data can influence the accuracy of the drawing of the gray-haze diffusion path.
The method comprises three steps of grid division, video image dust-haze change analysis, dust-haze diffusion path drawing and emission responsibility main body determination.
The content of each step is as follows:
1. grid division: and gridding and partitioning the region, and meanwhile, establishing a haze-free reference image sample library based on the video image for each grid.
2. Identifying the gray haze level of the image: through analyzing the video monitoring image, the haze-free reference image is combined, and meanwhile, fog and haze are distinguished based on time sequence analysis, and the haze index, the grade, the change trend and the change speed of the monitoring image at the current position are determined.
3. Drawing a dust-haze diffusion path and determining an emission responsibility subject: according to the location of the monitoring video, the dust-haze level, the time-varying trend and the large-area haze varying trend of the remote sensing image, drawing a dust-haze diffusion path varying with time, further obtaining a grid sequence on the path, and finally determining a specific pollution source grid.
The specific implementation mode is as follows:
the invention solves the problem of how to monitor the dust and haze directly from the main body in real time. Through the gridding of the urban area, a current dust-haze diffusion path is drawn in real time by adopting monitoring video haze level change analysis and time change analysis and global data analysis based on remote sensing images, and then specific dust-haze source grids are determined, and a source main body is determined. The implementation mode is as follows:
1. grid division: and determining the proper size of the grid according to the geographic position, and carrying out grid division on the area to be monitored. And (4) counting and acquiring road and video monitoring data in the grid area, and establishing an all-weather multi-temporal haze-free reference monitoring area image sample library.
2. Identifying the gray haze level of the image: (a) and determining the time interval of image acquisition and acquiring the monitoring video image in real time. (b) And removing the invalid image, extracting image contrast change, gradient change and visibility information, and determining the haze index of the haze image. (c) And comparing the current image with the haze-free reference image in the sample library to determine the dust-haze grade. And if the haze level of the image at the same place changes in a short time, judging that the image is a fog image and is not a haze image, and deleting the fog image. (d) And marking the video haze index, the grade, the time and the position information of the analyzed image and then storing the marked image into a database.
3. Acquiring a monitoring position and a time sequence: valuable monitoring video position and time information are selected, and specific position information and time information in the dust-haze diffusion path are obtained by combining the time change trend of the dust-haze grade information of the monitoring network points and the large-area dust-haze change monitoring of the remote sensing image.
4. Haze overall track analysis is carried out by utilizing the remote sensing image: and analyzing the haze remote sensing image by using a HYSPUT mode, and tracing the pollution source on a large scale.
5. Drawing a dust haze diffusion path: and drawing a dust-haze diffusion path changing along with time according to the obtained position with the time sequence characteristics, the video dust-haze index, the grade information and the remote sensing analysis data. The time of the drawing may be in units of hours, days, weeks.
6. Determination of the source grid: according to the dust-haze diffusion path and the dust-haze grade change process along with time, the grids where the dust-haze directly generates the source are found, so that the source subject range is reduced, and the responsibility subject is determined.
Claims (1)
1. A gray-haze diffusion path drawing and source determining method based on a monitoring video and a remote sensing image comprises three steps of grid division, video image gray-haze change analysis, gray-haze diffusion path drawing and emission responsibility main body determination;
wherein the meshing comprises:
determining the proper size of a grid according to the geographic position, carrying out gridding division on an area to be monitored, counting to obtain road and video monitoring data in the grid area, and establishing an all-weather multi-temporal haze-free reference monitoring area image database;
wherein the video image haze change analysis includes:
(a) determining the time interval of image acquisition, and acquiring a monitoring video image in real time; (b) removing invalid images, extracting image contrast change, gradient change and visibility information, and determining haze indexes of the haze images; (c) comparing the current image with a haze-free reference image in a sample library, determining a haze level, if the haze level of the image at the same place is changed in a short time, judging that the image is a fog image and not a haze image, and deleting the fog image; (d) labeling the video haze index, grade, time and position information of the analyzed image and storing the labeled video haze index, grade, time and position information into a database;
wherein the steps of drawing the haze diffusion path and determining the emission responsibility subject comprise:
(a) selecting valuable monitoring video position and time information, and obtaining specific position information and time information in a haze diffusion path by combining the haze index of a monitoring network point and the trend of the level information changing along with time;
(b) haze overall track analysis is carried out by utilizing the remote sensing image: analyzing the haze remote sensing image by using a HYSPUT mode, and tracing a pollution source on a large scale;
(c) drawing a dust haze diffusion path: drawing a dust-haze diffusion path which changes along with time according to the obtained position, dust-haze index, grade information and remote sensing analysis data with time series characteristics; the drawing time is in units of hours, days and weeks;
(d) determination of the source grid: and finding the source grids of the dust haze according to the dust haze diffusion path and the dust haze grade change process along with time, thereby reducing the range of the source main body and determining the responsibility main body.
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