PSLRC-Net: A PolInSAR and Spaceborne LiDAR Fusion Method for High-Precision DEM Inversion in Forested Areas

The Digital Elevation Model (DEM) is widely used in fields such as geoscience and environmental management. However, the existing DEMs struggle to meet the current requirements for timeliness and accuracy, especially in forested areas where vegetation cover can lead to overestimation of elevation. To address this issue, this paper proposes a PolInSAR and Spaceborne LiDAR Regression/Classification Network (PSLRC-Net) for refining external DEMs. Additionally, a forest/non-forest classification labeling method for spaceborne LiDAR footprints is introduced to provide labeled data for the classification branch during the training phase. PSLRC-Net adopts a multi-task learning framework and uses an expert selection mechanism based on a gating network to provide targeted support for the regression and classification branches. The regression branch consists of two task towers, and their outputs are weighted and fused by the output of the classification branch. This approach directs the regression branch to focus on the feature differences between forested and non-forested areas, resulting in more accurate elevation predictions. The network was trained on SAOCOM data from two sites, and the fitting results are evaluated for accuracy using an airborne LiDAR-derived DEM. Compared to different DEM datasets, the RMSE decreased by 51.7–64.6% and 51.9–63.7% at the two sites, while the MAE decreased by 55.5–66.8% and 55.5–68.6%. The experimental results confirm the validity of the model and demonstrate the potential of spaceborne LiDAR fusion with spaceborne PolInSAR to improve DEM accuracy.