Enhancing Photogrammetric DSM Based on Multiscale and Domain-Invariant Semantic Feature Learning

Despite the rapid development of stereo matching techniques, digital surface models (DSMs) reconstructed from disparities suffer from occlusion, solar shadow, and textureless ambiguity, thus failing to meet the requirements of 3D city modeling. To address this challenge, we proposed a DSM refinement network (RDSMNet) to improve the accuracy and completeness of photogrammetric DSMs. RDSMNet uptakes high-level semantic information from ortho-rectified stereo pairs using Transformer-based UNet architecture to preserve object structures of varying sizes. Meanwhile, a residual branch corrects the DSM values, which is combined with the semantic branch to generate the final refined result. Considering the significant diversity present in remote sensing images, we adopt the mixed-style technique to enhance the generalization capability of DSM refinement. To validate the performance of RDSMNet, we constructed a cross-city, multi-sensor, and multi-resolution dataset. Experimental results demonstrate that the proposed RDSMNet effectively enhances DSM elevation accuracy and structural integrity, exhibiting robust generalization performance across remote sensing data from various source domains. Compared with the current state-of-the-art method, the MAE of the proposed RDSMNet is reduced by 8.64%, and the annotation efficiency is improved from hour-level to minute-level relative to manual editing and involves no human intervention. Code and dataset will be available.