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Remote Sens. 2017, 9(7), 679; https://doi.org/10.3390/rs9070679

A Novel Building Type Classification Scheme Based on Integrated LiDAR and High-Resolution Images

1,2
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1,3,* , 1,3
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1,3
and
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1
Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2
Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA
3
Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
*
Author to whom correspondence should be addressed.
Academic Editors: Bailang Yu, Lei Wang, Qiusheng Wu and Prasad S. Thenkabail
Received: 26 April 2017 / Revised: 29 June 2017 / Accepted: 29 June 2017 / Published: 1 July 2017
(This article belongs to the Special Issue Remote Sensing for 3D Urban Morphology)
Full-Text   |   PDF [17150 KB, uploaded 3 July 2017]   |  

Abstract

Building type information is crucial to many urban studies, including fine-resolution population estimation, urban planning, and management. Although scientists have developed many methods to extract buildings via remote sensing data, only a limited number of them focus on further classification of the extracted results. This paper presents a novel building type classification scheme based on the integration of building height information from LiDAR, textural, spectral, and geometric information from high-resolution remote sensing images, and super-object information from the integrated dataset. Building height information is firstly extracted from LiDAR point clouds using a progressive morphological filter and then combined with high-resolution images for object-oriented segmentation. Multi-resolution segmentation of the combined image is performed to collect super-object information, which provides more information for classification in the next step. Finally, the segmentation results, as well as their super-object information, are inputted into the random forest classifier to obtain building type classification results. The classification scheme proposed in this study is tested through applications in two urban village areas, a type of slum-like land use characterized by dense buildings of different types, heights, and sizes, in Guangzhou, China. Segment level classification of the study area and validation area reached accuracies of 80.02% and 76.85%, respectively, while the building-level results reached accuracies of 98.15% and 87.50%, respectively. The results indicate that the proposed building type classification scheme has great potential for application in areas with multiple building types and complex backgrounds. This study also proves that both building height information and super-object information play important roles in building type classification. More accurate results could be obtained by incorporating building height information and super-object information and using the random forest classifier. View Full-Text
Keywords: building type classification; super-object information; LiDAR; high-resolution image; random forest; progressive morphological filter building type classification; super-object information; LiDAR; high-resolution image; random forest; progressive morphological filter
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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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Huang, Y.; Zhuo, L.; Tao, H.; Shi, Q.; Liu, K. A Novel Building Type Classification Scheme Based on Integrated LiDAR and High-Resolution Images. Remote Sens. 2017, 9, 679.

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