CAGFNet: A Cross-Attention Image-Guided Fusion Network for Disparity Estimation of High-Resolution Satellite Stereo Images
Qian Zhang, Jia Ge, Shufang Tian, Laidian Xi · Remote Sensing · 2025
Disparity estimation in high-resolution satellite stereo images is a critical task in remote sensing and photogrammetry. However, significant challenges arise due to the complexity of satellite stereo image scenes and the dynamic variations in disparities. Stereo matching becomes particularly difficult in areas with textureless regions, repetitive patterns, disparity discontinuities, and occlusions. Recent advancements in deep learning have opened new research avenues for disparity estimation. This paper presents a novel end-to-end disparity estimation network designed to address these challenges through three key innovations: (1) a cross-attention mechanism for robust feature extraction, (2) an image-guided module that preserves geometric details, and (3) a 3D feature fusion module for context-aware disparity refinement. Experiments on the US3D dataset demonstrate State-of-the-Art performance, achieving an endpoint error (EPE) of 1.466 pixels (14.71% D1-error) on the Jacksonville subset and 0.996 pixels (10.53% D1-error) on the Omaha subset. The experimental results confirm that the proposed network excels in disparity estimation, exhibiting strong learning capability and robust generalization performance.