DENSE MULTI-VIEW IMAGE MATCHING FOR DSM GENERATION FROM SATELLITE IMAGES

Abstract. Digital Surface Model (DSM) can be generated from stereo pairs of satellite or aerial images. Among the most state-of-the-art matching algorithms, Semi-Global Matching (SGM) has widely been used for generating DSM from both satellite and aerial images. This paper presents an approach to improve the accuracy of DSM generated by SGM from multi-view satellite images using a novel technique including several filters. The filters are used for deleting mismatches between very tall buildings in urban areas and removing the sea regions. The technique, in contrast to the recent multi-view matching approaches, considers some of the points generated with only a pair of images in the final DSM. The approach is implemented on five sequential high resolution images acquired by the Worldview-2 satellite. The results are locally evaluated in shape and quantitative terms in comparison with commercial software to reveal the capability of the approach to generate a reliable and dense point cloud. Experiments show that the proposed method can achieve below half-pixel accuracy.