Synergizing IFSAR and LiDAR data fusion using Delta Surface Fill for Hybrid DEMs accuracy in topographic mapping

Abstract

A Digital Elevation Model (DEM) continuously represents a ground surface landform created from photogrammetry and field surveys commonly used to produce topographic maps. However, obtaining aerial photography is costly and often fails to capture areas obstructed by clouds, mist, or haze due to the limitation of the optical systems. Consequently, the data collection process lacks efficiency and incurs high costs. Therefore, this study addresses the challenge of enhancing DEM accuracy through a fusion approach using the Delta Surface Fill (DSF) method to fulfill the need for accurate and detailed representations of topographic maps. The investigation focused on fusing Interferometric Synthetic Aperture Radar (IFSAR) and Light Detection and Ranging (LiDAR) data with distinct spatial resolutions and vertical accuracies to generate a Hybrid DEM. The DSF method was applied to fill gaps and reduce noise, ensuring a seamless fusion of datasets. Quantitative analysis unveiled a significant enhancement in vertical positional accuracy with the Hybrid DEM. The Root Mean Square Error (RMSE) for Hybrid DEM was reduced from 1.065 m to 0.312 m, signifying a remarkable 70.7% improvement over IFSAR DEM. Geomorphological assessments demonstrated the Hybrid DEM's aesthetic precision and spatial resolution superiority, contributing to sharper building edges and more explicit topographic features. Terrain profile analysis validated the robustness of the Hybrid DEM, showcasing strong agreement with LiDAR DEM across varying landscape conditions. The result proved that this study provides valuable insights for researchers and professionals engaged in geospatial data fusion, contributing to the advancement of topographic mapping and related applications.