Abstract
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.
Highlights
Digital Surface Model (DSM) is a digital model or 3D representation of a terrain's surface derived from various sources such as photogrammetry, LiDAR and IFSAR (Li et al, 2005)
This paper aims to improve both the accuracy and competences of the final DSM based on a novel approach in the multi-view matching algorithm
DSM can be generated from high resolution multi-view satellite images using Semi-Global Matching (SGM)
Summary
DSM is a digital model or 3D representation of a terrain's surface derived from various sources such as photogrammetry, LiDAR and IFSAR (Li et al, 2005). Researchers have used the multi-image techniques to generate a digital model from close range images (Ahmadabadian et al, 2013, Innmann et al, 2019), aerial (Ameri et al, 2002, Haala et al, 2012) and satellite images (Zhang et al, 2006, Krishna et al, 2008, d'Angelo et al, 2012, Giribabu et al, 2013, Qin, 2017, Gong et al, 2018). Most of the current approaches (d'Angelo et al, 2012, Haala et al, 2012) used SGM (Hirschmüller, 2008) for both pushbroom satellite and aerial images. SGM is performed through pixel-wise matching of Mutual Information (MI) and approximating a global, two dimensional (2D) smoothness constraint by combining eight or sixteen one dimensional (1D) constraints
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