This paper presents a segmentation-based stereo matching algorithm using an adaptive multi-cost approach, which is exploited for obtaining accuracy disparity maps. The main contribution is to integrate the appealing properties of multi-cost approach into the segmentation-based framework. Firstly, the reference image is segmented by using the mean-shift algorithm. Secondly, the initial disparity of each segment is estimated by an adaptive multi-cost method, which consists of a novel multi-cost function and an adaptive support window cost aggregation strategy. The multi-cost function increases the robustness of the initial raw matching costs calculation and the adaptive window reduces the matching ambiguity effectively. Thirdly, an iterative outlier suppression and disparity plane parameters fitting algorithm is designed to estimate the disparity plane parameters. Lastly, an energy function is formulated in segment domain, and the optimal plane label is approximated by belief propagation. The experimental results with the Middlebury stereo datasets, along with synthesized and real-world stereo images, demonstrate the effectiveness of the proposed approach.

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.