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

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...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 7; no. 4; pp. 4318 - 4342
Main Authors: Dou, Jie, Chang, Kuan-Tsung, Chen, Shuisen, Yunus, Ali, Liu, Jin-King, Xia, Huan, Zhu, Zhongfan
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.04.2015
Subjects:
Algorithms
Automation
genetic algorithm (GA)
Genetic algorithms
Image analysis
image classification
image segmentation
landslide detection
Landslides
Object oriented
object-oriented image analysis (OOIA)
Object-oriented programming
Reasoning
Remote sensing
Segmentation
Urban areas
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary: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.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs70404318