automatic case-based reasoning approach for landslide detection: integration of object-oriented image analysis and a genetic algorithm

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ID: 207945
2015
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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.
Reference Key
dou2015remoteautomatic Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Jie Dou;Kuan-Tsung Chang;Shuisen Chen;Ali P. Yunus;Jin-King Liu;Huan Xia;Zhongfan Zhu
Journal Journal of pharmacological sciences
Year 2015
DOI
10.3390/rs70404318
URL
http://www.mdpi.com/2072-4292/7/4/4318
Keywords
image classification image segmentation

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