Double Propagation Stereo Matching for Urban 3-D Reconstruction From Satellite Imagery

Abstract

Stereo matching is one of the popular methods to acquire the depth maps in 3-D reconstruction due to its high accuracy and completeness. However, traditional stereo matching methods are only suitable for general scenes or aerial images instead of urban scenes from large-scale satellite imageries. This article presents a novel double propagation stereo matching (DPSM) for urban 3-D reconstruction from satellite images. To make full use of the geometrical properties of man-made buildings, stereo-rectified satellite images, as input, are initialized to multiple superpixels with geometric models. Then, three similarity metrics are combined to calculate the weighted matching cost. A novel double propagation optimization is developed to optimize iteratively the weighted matching cost under the constraints of region boundaries and geometric models, and the disparity maps can be obtained by minimizing the energy function. Consequently, the corresponding dense 3-D point clouds and digital surface models (DSMs) are calculated by triangulation. Qualitative and quantitative experiments on stereo images captured by Pleiades and WorldView-2 satellites show that the proposed algorithm outperforms the most state-of-the-art stereo matching algorithms in terms of preserving depth discontinuous areas and restoring occlusions.