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

Yaping Chen; Junwen Zhang; Bao Wang; Jixing Li

doi:10.1117/12.3029765

21 June 2024 Accurate extraction method of surface subsidence information in mining area based on remote sensing image enhancement algorithm

Yaping Chen, Junwen Zhang, Bao Wang, Jixing Li

Author Affiliations +

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131670R (2024) https://doi.org/10.1117/12.3029765
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Yaping Chen, Junwen Zhang, Bao Wang, and Jixing Li "Accurate extraction method of surface subsidence information in mining area based on remote sensing image enhancement algorithm", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131670R (21 June 2024); https://doi.org/10.1117/12.3029765

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KEYWORDS

Vegetation

Mining

Error analysis

Data modeling

Remote sensing

Image enhancement

Land mines

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