Sparsity-driven Digital Terrain Model Extraction
This method addresses the problem of accurately extracting terrain models for applications requiring high-resolution ground surface data, such as urban planning or environmental monitoring.
This paper introduces an automatic method for extracting Digital Terrain Models (DTM) from high-resolution Digital Surface Models (DSM) using a variational framework. The method, called SD-DTM, employs an iterative approach to minimize a variational cost function, demonstrating its efficiency and effectiveness both visually and quantitatively on real-world DSM data.
We here introduce an automatic Digital Terrain Model (DTM) extraction method. The proposed sparsity-driven DTM extractor (SD-DTM) takes a high-resolution Digital Surface Model (DSM) as an input and constructs a high-resolution DTM using the variational framework. To obtain an accurate DTM, an iterative approach is proposed for the minimization of the target variational cost function. Accuracy of the SD-DTM is shown in a real-world DSM data set. We show the efficiency and effectiveness of the approach both visually and quantitatively via residual plots in illustrative terrain types.