RS-rSGM: a Revised Semi-Global Matching for remote sensing image

Jiangfan Liu; Hao He; Ying Nie; Jiarun Wang

doi:10.1117/12.2626650

20 December 2021 RS-rSGM: a Revised Semi-Global Matching for remote sensing image

Jiangfan Liu, Hao He, Ying Nie, Jiarun Wang

Proceedings Volume 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021); 1215506 (2021) https://doi.org/10.1117/12.2626650
Event: International Conference on Computer Vision, Application, and Design (CVAD 2021), 2021, Sanya, China

Abstract

Semi-Global Matching (SGM) algorithm is a conventional method in dense stereo matching and provides an acceptable result. Nevertheless, low accuracy and slow computation speed have been crucial factors restricting the processing of larger images. Meanwhile, similar texture, which appeared enormously on remote sensing images, ordinarily issues in the dilemma of computation failure. In this respect, the paper presents the method of stratifying and precis disparity search space by SGM pyramid and local invariant features to improve computational efficiency, reduce memory footprint and shrink the influence of similar textures, namely RS-rSGM. Experimental results indicate that RS-rSGM can efficiently improve the speed and reduce the time cost of computation on large resolution multi-similarity texture images.

Citation Download Citation

Jiangfan Liu, Hao He, Ying Nie, and Jiarun Wang "RS-rSGM: a Revised Semi-Global Matching for remote sensing image", Proc. SPIE 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021), 1215506 (20 December 2021); https://doi.org/10.1117/12.2626650

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KEYWORDS

Remote sensing

Transform theory

Optimization (mathematics)

Earth observing sensors

Image processing

Satellite imaging

Satellites

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