Digital surface model generation from aerial imagery using bridge probability relaxation matching

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.