To improve the quality and efficiency of Digital Elevation Model (DEM) construction in complex terrain, this study proposes a multi-source DEM acquisition and fusion method that integrates high-resolution optical imagery and interferometric Synthetic Aperture Radar (SAR) imagery. Using an unmanned aerial vehicle and satellite remote sensing system as a platform, this method constructs a multi-view data acquisition chain to generate optical imagery DEM and interferometric imagery SAR-DEM, respectively. By introducing a point cloud classification algorithm based on texture and structural features and a regional adaptive weight estimation model, weighted fusion of multi-source elevation data has been achieved. The fusion process employs error constraints and seamline control strategies to address typical challenges such as terrain occlusion, data holes, and elevation jumps. Experiments in representative landforms, including forests, glaciers, deserts, cities, and water bodies, demonstrates that this method has the characteristics of high elevation restoration accuracy and good boundary continuity, and can meet the three dimensions modeling needs of various landform types. Among them, the relative elevation mean error in hilly areas is 0.5 m. The research findings provide stable and reliable technical support for fields such as high-resolution topographic mapping, landform evolution monitoring, and disaster early warning, and are of great significance for promoting the automation and intelligence of remote sensing mapping.