Work

Sparsity-Driven Digital Terrain Model Extraction

Year: 2018

Type: conference-paper

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.

Source: IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

DOI: 10.1109/igarss.2018.8517569

Authors: Fatih Nar, Erdal Yilmaz, Gustau Camps-Valls

Institutions: Konya Food and Agriculture University, Universitat de València

Language: English

Topic: Satellite Image Processing and Photogrammetry

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, H2020 European Research Council

Awards: G1845633389, G563844271, Statistical Learning for Earth Observation Data Analysis.