Sparsity-driven Digital Terrain Model Extraction

TL;DR

This paper presents a novel sparsity-driven method for automatic high-resolution Digital Terrain Model extraction from Digital Surface Models, utilizing a variational framework and iterative optimization to improve accuracy and efficiency.

Contribution

The paper introduces SD-DTM, a new variational and iterative approach for DTM extraction that enhances accuracy and efficiency over existing methods.

Findings

Accurate DTM extraction demonstrated on real-world data

Method outperforms traditional approaches visually and quantitatively

Efficient iterative minimization improves computational performance

Abstract

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.