UGC-Net: Uncertainty-Guided Cost Volume Optimization with Contextual Features for Satellite Stereo Matching

Disparity estimation in satellite stereo images is a highly challenging task due to complex terrain, occlusions caused by tall buildings and structures, and texture-less regions such as roads, rivers, and building roofs. Recent deep learning-based satellite stereo disparity estimation methods have adopted cascade multi-scale feature extraction techniques to address these challenges. However, the recent learning-based methods still struggle to effectively estimate disparity in the high ambiguity regions. This paper proposes a disparity estimation and refinement method that leverages variance uncertainty in the cost volume to overcome these limitations. The proposed method calculates variance uncertainty from the cost volume and generates uncertainty weights to adjust the cost volume based on this information. These weights are designed to emphasize geometric features in regions with low uncertainty while enhancing contextual features in regions with high uncertainty, such as occluded or texture-less areas. Furthermore, the proposed method introduces a pseudo volume, referred to as the 4D context volume, which extends the reference image’s features during the stereo-matching aggregation step. By integrating the 4D context volume into the aggregation layer of the geometric cost volume, our method effectively addresses challenges in disparity estimation, particularly in occluded and texture-less areas. For the evaluation of the proposed method, we use the Urban Semantic 3D dataset and the WHU-Stereo dataset. The evaluation results show that the proposed method achieves state-of-the-art performance, improving disparity accuracy in challenging regions.