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Remote Sens. 2016, 8(12), 1030;

Building Change Detection Using Old Aerial Images and New LiDAR Data

School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Department of Civil, Environmental and Geodetic Engineering, the Ohio State University (OSU), Columbus, OH 43210, USA
Department of Electrical and Computer Engineering, the Ohio State University (OSU), Columbus, OH 43210, USA
Author to whom correspondence should be addressed.
Academic Editors: Jixian Zhang, Xiangguo Lin, Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 11 September 2016 / Revised: 12 December 2016 / Accepted: 14 December 2016 / Published: 17 December 2016
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
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Building change detection is important for urban area monitoring, disaster assessment and updating geo-database. 3D information derived from image dense matching or airborne light detection and ranging (LiDAR) is very effective for building change detection. However, combining 3D data from different sources is challenging, and so far few studies have focused on building change detection using both images and LiDAR data. This study proposes an automatic method to detect building changes in urban areas using aerial images and LiDAR data. First, dense image matching is carried out to obtain dense point clouds and then co-registered LiDAR point clouds using the iterative closest point (ICP) algorithm. The registered point clouds are further resampled to a raster DSM (Digital Surface Models). In a second step, height difference and grey-scale similarity are calculated as change indicators and the graph cuts method is employed to determine changes considering the contexture information. Finally, the detected results are refined by removing the non-building changes, in which a novel method based on variance of normal direction of LiDAR points is proposed to remove vegetated areas for positive building changes (newly building or taller) and nEGI (normalized Excessive Green Index) is used for negative building changes (demolish building or lower). To evaluate the proposed method, a test area covering approximately 2.1 km2 and consisting of many different types of buildings is used for the experiment. Results indicate 93% completeness with correctness of 90.2% for positive changes, while 94% completeness with correctness of 94.1% for negative changes, which demonstrate the promising performance of the proposed method. View Full-Text
Keywords: building change detection; aerial images; LiDAR; dense matching; graph cuts building change detection; aerial images; LiDAR; dense matching; graph cuts

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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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Du, S.; Zhang, Y.; Qin, R.; Yang, Z.; Zou, Z.; Tang, Y.; Fan, C. Building Change Detection Using Old Aerial Images and New LiDAR Data. Remote Sens. 2016, 8, 1030.

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