IAASNet: Ill-Posed-Aware Aggregated Stereo Matching Network for Cross-Orbit Optical Satellite Images

Stereo matching estimates disparity by finding correspondences between stereo image pairs. Under ill-posed conditions such as geometric differences, radiometric differences, and temporal changes, accurate estimation becomes difficult due to insufficient matching information. In remote sensing imagery, such ill-posed regions are more common because of complex imaging conditions. This problem is particularly pronounced in cross-track satellite stereo images, where existing methods often fail to effectively handle noise due to insufficient features or excessive reliance on prior assumptions. In this work, we propose an ill-posed-aware aggregated satellite stereo matching network, which integrates monocular depth estimation with an ill-posed-guided adaptive aware geometry fusion module to balance local and global features while reducing noise interference. In addition, we design an enhanced mask augmentation strategy during training to simulate occlusions and texture loss in complex scenarios, thereby improving robustness. Experimental results demonstrate that our method outperforms existing state-of-the-art approaches on the US3D dataset, achieving a 5.38% D1-error and 0.958 pixels endpoint error (EPE). In particular, our method shows significant advantages in ill-posed regions. Overall, the proposed network not only exhibits strong feature learning ability but also demonstrates robust generalization in real-world remote sensing applications.