High-Precision Reconstruction of Water Areas Based on High-Resolution Stereo Pairs of Satellite Images - Junyan Ye; Ruiqiu Xu; Yixiao Wang; Xu Huang (Remote Sensing, 2025)
The use of high-resolution satellite stereo pairs for dense image matching is a core technology for the low-cost generation of large-scale digital surface models (DSMs). However, water areas in satellite imagery often exhibit weak texture characteristics. This leads to serious issues in reconstructing water surface DSMs with traditional dense matching methods, such as significant holes and abnormal undulations. These problems significantly impact the intelligent application of satellite DSM products. To address these issues, this study innovatively proposes a water region DSM reconstruction method, boundary plane-constrained surface water stereo reconstruction (BPC-SWSR). The algorithm constructs a water surface reconstruction model with constraints on the plane’s tilt angle and boundary, combining effective ground matching data from the shoreline and the plane constraints of the water surface. This method achieves the seamless planar reconstruction of the water region, effectively solving the technical challenges of low geometric accuracy in water surface DSMs. This article conducts experiments on 10 high-resolution satellite stereo image pairs, covering three types of water bodies: river, lake, and sea. Ground truth water surface elevations were obtained through a manual tie point selection followed by forward intersection and planar fitting in water surface areas, establishing a rigorous validation framework. The DSMs generated by the proposed algorithm were compared with those generated by state-of-the-art dense matching algorithms and the industry-leading software Reconstruction Master version 6.0. The proposed algorithm achieves a mean RMSE of 2.279 m and a variance of 0.6613 m2 in water surface elevation estimation, significantly outperforming existing methods with average RMSE and a variance of 229.2 m and 522.5 m2, respectively. This demonstrates the algorithm’s ability to generate more accurate and smoother water surface models. Furthermore, the algorithm still achieves excellent reconstruction results when processing different types of water areas, confirming its wide applicability in real-world scenarios.