Sparsity-driven Digital Terrain Model Extraction
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 iterat...
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| creator | Nar, Fatih Yilmaz, Erdal Camps-Valls, Gustau |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2012.08639 |
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| subjects | Computer Science - Computer Vision and Pattern Recognition Cost function High resolution Iterative methods Sparsity Terrain models |
| title | Sparsity-driven Digital Terrain Model Extraction |
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