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Remote Sens. 2017, 9(4), 349; doi:10.3390/rs9040349

A Fuzzy-GA Based Decision Making System for Detecting Damaged Buildings from High-Spatial Resolution Optical Images

Faculty of Geodesy & Geomatics Engineering, K. N. Toosi University of Technology, Tehran 1996715433, Iran
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Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez, Soe Myint and Prasad S. Thenkabail
Received: 14 January 2017 / Revised: 27 March 2017 / Accepted: 1 April 2017 / Published: 20 April 2017
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
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Abstract

In this research, a semi-automated building damage detection system is addressed under the umbrella of high-spatial resolution remotely sensed images. The aim of this study was to develop a semi-automated fuzzy decision making system using Genetic Algorithm (GA). Our proposed system contains four main stages. In the first stage, post-event optical images were pre-processed. In the second stage, textural features were extracted from the pre-processed post-event optical images using Haralick texture extraction method. Afterwards, in the third stage, a semi-automated Fuzzy-GA (Fuzzy Genetic Algorithm) decision making system was used to identify damaged buildings from the extracted texture features. In the fourth stage, a comprehensive sensitivity analysis was performed to achieve parameters of GA leading to more accurate results. Finally, the accuracy of results was assessed using check and test samples. The proposed system was tested over the 2010 Haiti earthquake (Area 1 and Area 2) and the 2003 Bam earthquake (Area 3). The proposed system resulted in overall accuracies of 76.88 ± 1.22%, 65.43 ± 0.29%, and 90.96 ± 0.15% over Area 1, Area 2, and Area 3, respectively. On the one hand, based on the concept of the proposed Fuzzy-GA decision making system, the automation level of this system is higher than other existing systems. On the other hand, based on the accuracy of our proposed system and four advanced machine learning techniques, i.e., bagging, boosting, random forests, and support vector machine, in the detection of damaged buildings, it seems that our proposed system is robust and efficient. View Full-Text
Keywords: building damage detection; Fuzzy-GA decision making system; machine learning techniques; optical remotely sensed images; sensitivity analysis; texture analysis building damage detection; Fuzzy-GA decision making system; machine learning techniques; optical remotely sensed images; sensitivity analysis; texture analysis
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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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Janalipour, M.; Mohammadzadeh, A. A Fuzzy-GA Based Decision Making System for Detecting Damaged Buildings from High-Spatial Resolution Optical Images. Remote Sens. 2017, 9, 349.

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