Multi-source data joint processing framework for DEM calibration and fusion

Abstract

High-accuracy digital elevation models (DEMs) are essential for remote sensing and geospatial analysis, yet integrating multi-source data over large and complex terrains remains challenging. To address these challenges, this study presents the Multi-source Data Joint Processing (MDJP) framework. This framework establishes a systematic way for correcting DEM errors of varying quality and integrating multi-source data, leveraging deep learning-based calibration and spatially adaptive fusion techniques to enhance DEM accuracy and consistency in large and complex regions. For calibration, our proposed DEM calibration model (DemFormer) combines a lightweight Transformer module with a bagging decision-tree network in a stacking framework, specifically designed to enhance the stability and accuracy of DEM elevation error predictions. For fusion, our DEM fusion model (DemFusion) employs spatial autocorrelation analysis and KD-Tree clustering to compute optimal fusion weights, effectively integrating complementary elevation information from multiple DEM sources. We evaluate the MDJP framework using four widely used global DEMs—Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X), Advanced Land Observing Satellite World 3D-30 m (AW3D30)—each at 1-arc second (∼30 m) resolution. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) elevation data serves as the independent reference dataset for assessment. Our experiments, conducted in Guangdong Province, China, and the Northern Territory of Australia, demonstrate that the DemFormer model reduces the root mean square error (RMSE) by 18.38 %, 17.28 %, 54.53 %, and 65.24 % for TanDEM-X, AW3D30, SRTM, and ASTER, respectively. Furthermore, the DemFusion model further refines the results, and the fused DEM had better accuracy than other DEMs in Guangdong and the Northern Territory. These findings underscore the robustness of our approach and establish a new benchmark for DEM calibration and fusion, with significant implications for geospatial analysis and environmental monitoring.

Keywords

• Digital Elevation Model (DEM)
• Large and complex region
• Deep learning
• DEM accuracy enhancement