Stereo Matching and Digital Surface Model Generation for Satellite Imagery: From Scanline Aggregation to Deep Learning with RAFTStereo

Abstract. Digital Surface Model (DSM) generation from satellite stereo images is one of the key applications in both computer vision and photogrammetry. Recent progress in high-resolution satellite imaging and deep learning has favoured their applications for accuracy enhancement and automation in DSM generation. However, complex acquisition geometries from satellite imaging, regions of repetitive textures or patterns, and varying atmospheric conditions continue to complicate the process of dense stereo matching. This study presents a detailed framework for DSM generation: image preprocessing, epipolar rectification, disparity estimation, and 3D reconstruction. At the image matching stage of stereo images, it compares the traditional methods such as Semi-Global Matching (SGM) and More Global Matching (MGM) with a deep learning-based approach-RAFTStereo. Experimental results with WorldView-3 satellite stereo pairs of the Data Fusion Contest 2019 (DFC2019) dataset show that while SGM and MGM remain robust and computationally efficient, RAFTStereo performs better especially on radiometrically and geometrically complex scenes. MGM provides numerical errors at the lowest values ≈2–4 meters, while RAFTStereo offers more coherent disparity maps, with more smooth surfaces, and fewer artifacts. These results also point out the complementary nature of traditional approaches and learning-based methodologies.