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Remote Sens. 2016, 8(9), 730;

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

School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China
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
(This article belongs to the Special Issue Airborne Laser Scanning)
Full-Text   |   PDF [13280 KB, uploaded 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 is complex. This paper proposes a new method for ground point extraction based on deep learning using deep convolutional neural networks (CNN). For every point with spatial context, the neighboring points within a window are extracted and transformed into an image. Then, the classification of a point can be treated as the classification of an image; the point-to-image transformation is carefully crafted by considering the height information in the neighborhood area. After being trained on approximately 17 million labeled ALS points, the deep CNN model can learn how a human operator recognizes a point as a ground point or not. The model performs better than typical existing algorithms in terms of error rate, indicating the significant potential of deep-learning-based methods in feature extraction from a point cloud. View Full-Text
Keywords: deep learning; convolutional neural network (CNN); digital terrain model (DTM); ALS; ground point classification deep learning; convolutional neural network (CNN); digital terrain model (DTM); ALS; ground point classification

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Hu, X.; Yuan, Y. Deep-Learning-Based Classification for DTM Extraction from ALS Point Cloud. Remote Sens. 2016, 8, 730.

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