Detecting Damaged Building Regions Based on Semantic Scene Change from Multi-Temporal High-Resolution Remote Sensing ImagesReport as inadecuate


Detecting Damaged Building Regions Based on Semantic Scene Change from Multi-Temporal High-Resolution Remote Sensing Images


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1

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

2

College of Electronic and Information, Yangtze University, Jingzhou 434023, China

3

Military Region of Hubei Province, Wuhan 430079, China





*

Author to whom correspondence should be addressed.



Academic Editors: Milan Konecny and Wolfgang Kainz

Abstract The detection of damaged building regions is crucial to emergency response actions and rescue work after a disaster. Change detection methods using multi-temporal remote sensing images are widely used for this purpose. Differing from traditional methods based on change detection for damaged building regions, semantic scene change can provide a new point of view since it can indicate the land-use variation at the semantic level. In this paper, a novel method is proposed for detecting damaged building regions based on semantic scene change in a visual Bag-of-Words model. Pre- and post-disaster scene change in building regions are represented by a uniform visual codebook frequency. The scene change of damaged and non-damaged building regions is discriminated using the Support Vector Machine SVM classifier. An evaluation of experimental results, for a selected study site of the Longtou hill town of Yunnan, China, which was heavily damaged in the Ludian earthquake of 14 March 2013, shows that this method is feasible and effective for detecting damaged building regions. For the experiments, WorldView-2 optical imagery and aerial imagery is used. View Full-Text

Keywords: detection of damaged building region; scene classification; scene change; visual bag of words; SVM detection of damaged building region; scene classification; scene change; visual bag of words; SVM





Author: Jihui Tu 1,2, Deren Li 1, Wenqing Feng 1, Qinhu Han 3 and Haigang Sui 1,*

Source: http://mdpi.com/



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