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Sensors (Basel). 2018 Feb 3;18(2). pii: E448. doi: 10.3390/s18020448.

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

Author information

1
Department of Geodesy and Geoinformation, Technische Universität Wien, Gußhausstraße 27-29, 1040 Vienna, Austria. tranthihuonggiang@humg.edu.vn.
2
Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Hanoi 10000, Vietnam. tranthihuonggiang@humg.edu.vn.
3
Department of Geodesy and Geoinformation, Technische Universität Wien, Gußhausstraße 27-29, 1040 Vienna, Austria. camillo.ressl@geo.tuwien.ac.at.
4
Department of Geodesy and Geoinformation, Technische Universität Wien, Gußhausstraße 27-29, 1040 Vienna, Austria. norbert.pfeifer@geo.tuwien.ac.at.

Abstract

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.

KEYWORDS:

LiDAR; change classification; machine learning

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