Novel method and accuracy evaluation for GB-SAR image geocoding of open-pit mines based on laser point cloud

Wang Cao (), Yachun Mao (), Jie Wen (), Xinqi Mao (), Mengyuan Xu (), Liming He () and Jing Liu ()
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Wang Cao: Shenyang Jianzhu University
Yachun Mao: Northeastern University
Jie Wen: Northeastern University
Xinqi Mao: Northeastern University
Mengyuan Xu: Northeastern University
Liming He: Northeastern University
Jing Liu: Northeastern University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 14, No 39, 17129-17151

Abstract: Abstract Ground-based synthetic aperture radar (GB-SAR) is widely used in several monitoring fields for its advantages of high deformation sensitivity. However, the limitations of its two-dimensional sector imaging mode make it difficult to accurately analyze and decipher its high-precision deformation results in a three-dimensional (3D) form, which has caused troubles in locating hazardous areas of open-pit mine slopes and disaster warning. For this purpose, this paper uses the laser point cloud as auxiliary data and performs coordinate definition and coordinate conversion on GB-SAR images, on the basis of which an image geocoding method that takes into account the original 3D point cloud matching is proposed. Firstly, the original 3D point cloud coordinate matching was performed based on the sector mesh. Then, for the pixels not matched to the point cloud, their planar coordinates x and y were reconstructed using the center of gravity weighted algorithm with the pixel center coordinates as the reference, and further, the elevation h was reconstructed based on the planar coordinate information using a radial basis function neural network. Finally, the accuracy of the pixels’ 3D coordinate reconstruction was quantitatively evaluated in the absence of the matched point cloud. The application of GB-SAR landslide monitoring in the Nanfen open-pit mine in Liaoning, China, verifies the reliability of this paper’s method, and its distance alignment root mean square error of 9.89 cm. This study can provide technical support for the interpretation of hazardous areas in open-pit mines and early warning of landslide disasters.

Keywords: Open-pit mines; GB-SAR image geocoding; Landslide warning; Center of gravity weighted; RBF neural network (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07469-9

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