@inproceedings{b88756514f9041119c96f6bb6a9f9fe3,
title = "High-Precision Matching of Multisource Remote Sensing Images Considering Geometric Constraints",
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.",
keywords = "DEM, Geometric Constraints, Least Square Image Matching, Overall Probabilistic Relaxation Methods",
author = "Xianghao Kong and Baiyang Hu and Qiong Wu and Zhuoyi Chen and Hua Yang and Kun Gao",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 2024 Applied Optics and Photonics China: Optical Sensing, Imaging Technology, and Applications, AOPC 2024 ; Conference date: 23-07-2024 Through 26-07-2024",
year = "2024",
doi = "10.1117/12.3047490",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Yadong Jiang and Bin Xue",
booktitle = "AOPC 2024",
address = "United States",
}