SIGLNet: a semantic information-guided lightweight stereo matching network for satellite imagery

Aerospace photogrammetry serves as a critical methodology for large-scale 3D reconstruction, where disparity estimation constitutes the fundamental technology. With various types of complex scenes covered in high-resolution satellite images, traditional stereo matching methods are facing a great challenge. Although deep learning-based disparity estimation networks have gained significant traction in recent years, research within satellite image stereo matching predominantly prioritizes model accuracy while neglecting computational efficiency and model complexity, which runs counter to the general trend of onboard intelligent processing in modern remote sensing satellites. To bridge this gap, we propose SIGLNet, a semantic information-guided lightweight end-to-end stereo matching network specifically designed for high-resolution satellite stereo imagery. The SIGLNet first employs a lightweight backbone to extract multi-scale deep features, which are fused into the downsampled low-resolution feature maps. Utilizing these representations, we then construct a low-resolution cost volume and deploy efficient cost aggregation via inverted residual blocks and multi-branch adjustable bottleneck modules, producing an initial low-resolution disparity map. Finally, a semantic information-guided disparity upsampling module reconstructs high-quality full-resolution disparity maps. Extensive experiments conducted on the US3D and the WHUStereo benchmark demonstrate that the proposed SIGLNet achieves the highest disparity estimation accuracy while maintaining competitive model complexity compared to widely used methods.