Sparsity-driven Digital Terrain Model Extraction
Computer Vision and Pattern Recognition
2020-12-17 v1
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.
Cite
@article{arxiv.2012.08639,
title = {Sparsity-driven Digital Terrain Model Extraction},
author = {Fatih Nar and Erdal Yilmaz and Gustau Camps-Valls},
journal= {arXiv preprint arXiv:2012.08639},
year = {2020}
}
Comments
Preprint. Paper published in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, 2018, pp. 1316-1319