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周琪在JARS上发表题为“Digital surface model generation from aerial imagery using bridge probability relaxation matching”的SCI期刊论文

发布时间:2022-12-23 07:38 作者: 来源: 阅读:

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Qi Zhou, Yansong Duan, Xinbo Zhao, Jieke Dong, Hao Zhang, and Hui Cao "Digital surface model generation from aerial imagery using bridge probability relaxation matching," Journal of Applied Remote Sensing 16(4), 046511 (6 December 2022). https://doi.org/10.1117/1.JRS.16.046511




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6 December 2022 Digital surface model generation from aerial imagery using bridge probability relaxation matching
Qi Zhou,           Yansong Duan,           Xinbo Zhao,           Jieke Dong,           Hao Zhang,           Hui Cao          
Author Affiliations +        
Journal of Applied Remote Sensing, Vol. 16, Issue 4, 046511 (December 2022).             https://doi.org/10.1117/1.JRS.16.046511            
           
Abstract

Optical image dense matching is a crucial step in the process of generating digital surface models (DSMs). Many existing dense matching methods have adopted pixelwise matching strategy and have achieved precise matching results; however, the methods are time consuming and have limited efficiency in high surveying and mapping production. We introduce a bridge probability relaxation matching method for automatic DSM generation. The method adopts a coarse-to-fine hierarchical strategy and achieves high matching accuracy and fast processing speed simultaneously. This method builds a self-adaptive disparity surface model in a local area and constrains the disparity surface using the spatial relationship between feature points and adjacent pixels. Finally, the disparity is optimized by calculating the increment of the relaxation iteration probability. Experiments are based on different areas with different textures and terrain types. Compared with the DSMs derived from semi-global matching, our proposed approach achieves high levels of accuracy and efficiency in automatic DSM generation.