An Adaptive Dense Matching Method for Airborne Images Using Texture Information

doi:10.11947/j.AGCS.2017.20150608

Abstract

Abstract: Semi-global matching (SGM) is essentially a discrete optimization for the disparity value of each pixel, under the assumption of disparity continuities. SGM overcomes the influence of the disparity discontinuities by a set of parameters. Using smaller parameters, the continuity constraint is weakened, which will cause significant noises in planar and textureless areas, reflected as the fluctuations on the final surface reconstruction. On the other hands, larger parameters will impose too much constraints on continuities, which may lead to losses of sharp features. To address this problem, this paper proposes an adaptive dense stereo matching methods for airborne images using with texture information. Firstly, the texture is quantified, and under the assumption that disparity variation is directly proportional to the texture information, the adaptive parameters are gauged accordingly. Second, SGM is adopted to optimize the discrete disparities using the adaptively tuned parameters. Experimental evaluations using the ISPRS benchmark dataset and images obtained by the SWDC-5 have revealed that the proposed method will significantly improve the visual qualities of the point clouds.

Key words: dense image matching, semi-global matching, texture feature, adaptive method

CLC Number:

P231

ZHU Qing, CHEN Chongtai, HU Han, DING Yulin. An Adaptive Dense Matching Method for Airborne Images Using Texture Information[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(1): 62-72.

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