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Remote Sens. 2015, 7(4), 4318-4342; doi:10.3390/rs70404318

Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

1
Department of Natural Environmental Studies, The University of Tokyo, Kashiwa 277-8568, Japan
2
Department of Civil Engineering and Environmental Informatics, Minghsin University of Science and Technology, Hsin-Chu 304, Taiwan
3
Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, China
4
LiDAR Technology Co., Zhubei, Hsin-Chu 302, Taiwan
5
Guizhou University of Finance and Economics, Huaxi District, Guiyang 550025, China
6
College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Yu-Chang Chan and Prasad S. Thenkabail
Received: 13 November 2014 / Revised: 12 March 2015 / Accepted: 3 April 2015 / Published: 13 April 2015
(This article belongs to the Special Issue Remote Sensing in Geology)
View Full-Text   |   Download PDF [3013 KB, uploaded 20 April 2015]   |  

Abstract

This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature optimization; and (3) detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87) demonstrates higher classification performance than the stand-alone OOIA (0.75) method for detecting landslides. The area under curve (AUC) value was also higher than that of the simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes. View Full-Text
Keywords: genetic algorithm (GA); image classification; image segmentation; landslide detection; object-oriented image analysis (OOIA) genetic algorithm (GA); image classification; image segmentation; landslide detection; object-oriented image analysis (OOIA)
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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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MDPI and ACS Style

Dou, J.; Chang, K.-T.; Chen, S.; Yunus, A.P.; Liu, J.-K.; Xia, H.; Zhu, Z. Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm. Remote Sens. 2015, 7, 4318-4342.

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