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Remote Sens. 2019, 11(7), 848; https://doi.org/10.3390/rs11070848

A Building Detection Method Based on Semi-Suppressed Fuzzy C-Means and Restricted Region Growing Using Airborne LiDAR

1, 1,2,* and 3,*
1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Department of Oceanography, Dalhousie University, Halifax, NS B3H 4R2, Canada
3
Faculty of Resources and Environmental Science, Hubei University, Wuhan 430068, China
*
Authors to whom correspondence should be addressed.
Received: 12 February 2019 / Revised: 5 April 2019 / Accepted: 6 April 2019 / Published: 8 April 2019
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Abstract

Building detection using airborne Light Detection And Ranging (LiDAR) data is the essential prerequisite of many applications, including three-dimensional city modeling. In the paper, we propose a coarse-to-fine building detection method that is based on semi-suppressed fuzzy C-means and restricted region growing. Based on a filtering step, the remaining points can be separated into two groups by semi-suppressed fuzzy C-means. The group contains points that are located on building roofs that form a building candidate set. Subsequently, a restricted region growing algorithm is implemented to search for more building points. The proposed region growing method perfectly ensures the rapid growth of building regions and slow growth of non-building regions, which enlarges the area differences between building and non-building regions. A two-stage strategy is then adopted to remove tiny point clusters with small areas. Finally, a minimum bounding rectangle (MBR) is used to supplement the building points and refine the results of building detection. Experimental results on five datasets, including three datasets that were provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) and two Chinese datasets, verify that most buildings and non-buildings can be well separated during our coarse building detection process. In addition, after refined processing, our proposed method can offer a high success rate for building detection, with over 89.5% completeness and a minimum 91% correctness. Hence, various applications can exploit our proposed method. View Full-Text
Keywords: airborne LiDAR; building detection; fuzzy C-means; region growing; filtering airborne LiDAR; building detection; fuzzy C-means; region growing; filtering
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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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Cai, Z.; Ma, H.; Zhang, L. A Building Detection Method Based on Semi-Suppressed Fuzzy C-Means and Restricted Region Growing Using Airborne LiDAR. Remote Sens. 2019, 11, 848.

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