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Sensors 2018, 18(2), 448;

Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds

Department of Geodesy and Geoinformation, Technische Universität Wien, Gußhausstraße 27-29, 1040 Vienna, Austria
Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Hanoi 10000, Vietnam
Author to whom correspondence should be addressed.
Received: 19 December 2017 / Revised: 30 January 2018 / Accepted: 31 January 2018 / Published: 3 February 2018
(This article belongs to the Section Remote Sensors)
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This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating to relative terrain elevation, features specific for the multi-target capability of laser scanning, and features combining the point clouds of both epochs to identify the change. All these features are merged in the points and then training samples are acquired to create the model for supervised classification, which is then applied to the whole study area. The final results reach an overall accuracy of over 90% for both epochs of eight classes: lost tree, new tree, lost building, new building, changed ground, unchanged building, unchanged tree, and unchanged ground. View Full-Text
Keywords: LiDAR; change classification; machine learning LiDAR; change classification; machine learning

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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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Tran, T.H.G.; Ressl, C.; Pfeifer, N. Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds. Sensors 2018, 18, 448.

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