High-precision matching of multisource remote sensing images considering geometric constraints

Xianghao Kong; Baiyang Hu; Qiong Wu; Zhuoyi Chen; Hua Yang; Kun Gao

doi:10.1117/12.3047490

13 December 2024 High-precision matching of multisource remote sensing images considering geometric constraints

Xianghao Kong, Baiyang Hu, Qiong Wu, Zhuoyi Chen, Hua Yang, Kun Gao

Author Affiliations +

Proceedings Volume 13496, AOPC 2024: Optical Sensing, Imaging Technology, and Applications; 134960N (2024) https://doi.org/10.1117/12.3047490
Event: Applied Optics and Photonics China 2024 (AOPC2024), 2024, Beijing, China

Abstract

The traditional correlation coefficient matching method can achieve good image matching results for multi-source satellite images under plain terrain conditions. However, satellite images under mountainous and other complex terrain conditions are prone to mismatches. This article proposes a global probability relaxation automatic matching algorithm that takes into account geometric constraints. Based on the correlation coefficient criterion, combined with least squares matching and coarse to fine matching strategies, the algorithm utilizes the global probability relaxation conditions considering geometric constraints to achieve high-precision matching of multi-source images. A DEM automatic extraction algorithm based on this method is designed. The results of the multi-source image matching and the automatic generation of DEM by using this method are analyzed to validate the effectiveness of the algorithm.

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

Citation Download Citation

Xianghao Kong, Baiyang Hu, Qiong Wu, Zhuoyi Chen, Hua Yang, and Kun Gao "High-precision matching of multisource remote sensing images considering geometric constraints", Proc. SPIE 13496, AOPC 2024: Optical Sensing, Imaging Technology, and Applications, 134960N (13 December 2024); https://doi.org/10.1117/12.3047490

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