Research Papers

Constructions detection from unmanned aerial vehicle images using random forest classifier and histogram-based shape descriptor

[+] Author Affiliations
Bo Yu

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, State Key Laboratory of Remote Sensing Science, Datun Road, Beijing 100101, China

Graduate University of Chinese Academy of Sciences, Yuquan Road, Beijing 100049, China

Li Wang

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, State Key Laboratory of Remote Sensing Science, Datun Road, Beijing 100101, China

Zheng Niu

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, State Key Laboratory of Remote Sensing Science, Datun Road, Beijing 100101, China

Muhammd Shakir

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, State Key Laboratory of Remote Sensing Science, Datun Road, Beijing 100101, China

Graduate University of Chinese Academy of Sciences, Yuquan Road, Beijing 100049, China

Xiaoqi Liu

Seagate Technology Co., Ltd., 7801 Computer Avenue, Bloomington, Minnesota, United States

J. Appl. Remote Sens. 8(1), 083554 (Sep 09, 2014). doi:10.1117/1.JRS.8.083554
History: Received November 10, 2013; Revised July 8, 2014; Accepted August 18, 2014
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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.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Bo Yu ; Li Wang ; Zheng Niu ; Muhammd Shakir and Xiaoqi Liu
"Constructions detection from unmanned aerial vehicle images using random forest classifier and histogram-based shape descriptor", J. Appl. Remote Sens. 8(1), 083554 (Sep 09, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083554


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