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Journal of Applied Remote Sensing

Constructions detection from unmanned aerial vehicle images using random forest classifier and histogram-based shape descriptor
Author(s): Bo Yu; Li Wang; Zheng Niu; Muhammd Shakir; Xiaoqi Liu
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Paper Abstract

Remotely sensed data, especially unmanned aerial vehicle images, provide more details about intensive ground objects. An algorithm with a solid capability to effectively handle this massive information is highly desired. The state-of-the-art algorithms proposed for building detection mainly focus only on buildings in use, ignoring those under construction. For buildings under construction, various types of soil are the main obstructions that impede building identification. Unmanned aerial vehicle images are used as experimental data for discriminating constructions (both in use and under construction) from other ground objects. A mask for potential constructions is created before the exact detection. A random forest classifier, together with a high dimensional textural feature, is used to remove soils that share similar texture characteristics with constructions. Experimental results suggest that our method can be widely used to detect construction (both in use and under construction) and has the ability to effectively handle heavy amounts of information from large-scale images with very high spatial resolution. It provides a method for soil exclusion from remotely sensed images with very high resolution.

Paper Details

Date Published: 9 September 2014
PDF: 17 pages
J. Appl. Remote Sens. 8(1) 083554 doi: 10.1117/1.JRS.8.083554
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Bo Yu, Institute of Remote Sensing and Digital Earth (China)
Graduate Univ. of Chinese Academy of Sciences (China)
Li Wang, Institute of Remote Sensing and Digital Earth (China)
Zheng Niu, Institute of Remote Sensing and Digital Earth (China)
Muhammd Shakir, Institute of Remote Sensing and Digital Earth (China)
Graduate Univ. of Chinese Academy of Sciences (China)
Xiaoqi Liu, Seagate Technology LLC (United States)


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