Accurate extraction method of surface subsidence information in mining area based on remote sensing image enhancement algorithm

The article takes the coal mining subsidence area of a mine in northern Shaanxi Province as a test site, and constructs the initial subsidence model by utilizing the aerial image data from a low-altitude UAV. The visible band difference vegetation index (VDVI) is used to eliminate the subsidence data of the vegetation covered area, which has a large influence on the accuracy of the subsidence model, from the initial subsidence model, and then combined with the remote sensing image enhancement algorithm to fit the subsidence information of the vegetation area, so as to generate the subsidence model of the ground surface with the influence of the vegetation eliminated. The modeling method of remote sensing image enhancement algorithm after removing noise and correcting the influence of systematic error can meet the basic requirements of large-scale and high-intensity coal mining subsidence monitoring in western low-vegetation-covered areas, which provides a feasible solution for the use of UAV remote sensing technology for efficient monitoring and fine modeling of coal mining subsidence in western mining areas.