Deep-Learning-Based Classification for DTM Extraction from ALS Point Cloud

Open AccessThis article is freely available re-usable Remote Sens. 2016, 8(9), 730; doi:10.3390/rs8090730 (registering DOI) Article 1 School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China 2 Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China * Author to whom correspondence should be addressed. Academic Editors: Jie Shan, Juha Hyyppä, Lars T. Waser, Xiaofeng Li and Prasad S. Thenkabail Received: 20 May 2016 / Revised: 21 August 2016 / Accepted: 29 August 2016 / Published: 5 September 2016 Airborne laser scanning (ALS) point cloud data are suitable for digital terrain model (DTM) extraction given its high accuracy in elevation. Existing filtering algorithms that eliminate non-ground points mostly depend on terrain feature assumptions or representations; these assumptions result in errors when the scene…


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