Unsupervised Detection of Earthquake-Triggered Roof-Holes From UAV Images Using Joint Color and Shape Features

Many methods have been developed to detect damaged buildings due to earthquake. However, little attention has been paid to analyze slightly affected buildings. In this letter, an unsupervised method is presented to detect earthquake-triggered “roof-holes” on rural houses from unmanned aerial vehicle (UAV) images. First, both orthomosaic and gradient images are generated from a set of UAV images. Then, a modified Chinese restaurant franchise model is used to learn an unsupervised model of the geo-object classes in the area by fusing both oversegmented orthomosaic and gradient images. Finally, “roof-holes” on rural houses are detected using the learned model. The performance of the proposed method is evaluated in terms of both qualitative and quantitative indexes.

[1]  G. Grünthal.  European macroseismic scale 1998 : EMS-98 , 1998 .

[2]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[3]  Hajime Mitomi,et al.  Development of automated extraction method for building damage area based on maximum likelihood classifier , 2001 .

[4]  Dimitris N. Metaxas,et al.  Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions , 2003, IEEE Transactions on Medical Imaging.

[5]  R. Spence,et al.  Using High-Resolution Satellite Images for Post-Earthquake Building Damage Assessment: A Study following the 26 January 2001 Gujarat Earthquake , 2004 .

[6]  M. Turker,et al.  Automatic detection of earthquake‐damaged buildings using DEMs created from pre‐ and post‐earthquake stereo aerial photographs , 2005 .

[7]  M. Matsuoka,et al.  Visual Damage Interpretation of Buildings in Bam City using QuickBird Images following the 2003 Bam, Iran, Earthquake , 2005 .

[8]  M. Matsuoka,et al.  Building Damage Mapping of the 2003 Bam, Iran, Earthquake Using Envisat/ASAR Intensity Imagery , 2005 .

[9]  Michael I. Jordan,et al.  Hierarchical Dirichlet Processes , 2006 .

[10]  Fabio Dell'Acqua,et al.  Rapid Damage Detection in the Bam Area Using Multitemporal SAR and Exploiting Ancillary Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Henri Maître,et al.  Kernel MDL to Determine the Number of Clusters , 2007, MLDM.

[12]  Mustafa Turker,et al.  Building‐based damage detection due to earthquake using the watershed segmentation of the post‐event aerial images , 2008 .

[13]  Zhang Wenjuan.  Assessment and analysis of collapsing houses by aerial images in the Wenchuan earthquake , 2010, National Remote Sensing Bulletin.

[14]  Peijun Li,et al.  Urban building damage detection from very high resolution imagery using OCSVM and spatial features , 2010 .

[15]  Rama Chellappa,et al.  Entropy rate superpixel segmentation , 2011, CVPR 2011.

[16]  Yunhao Chen,et al.  An Object-Oriented Semantic Clustering Algorithm for High-Resolution Remote Sensing Images Using the Aspect Model , 2011, IEEE Geoscience and Remote Sensing Letters.

[17]  X. Tong,et al.  Building-damage detection using pre- and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake , 2012 .

[18]  Steven E. Franklin,et al.  A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery , 2012 .

[19]  Jie Shan,et al.  A comprehensive review of earthquake-induced building damage detection with remote sensing techniques , 2013 .

[20]  David A. Clausi,et al.  A Multiscale Latent Dirichlet Allocation Model for Object-Oriented Clustering of VHR Panchromatic Satellite Images , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[21]  X. Tong,et al.  Use of shadows for detection of earthquake-induced collapsed buildings in high-resolution satellite imagery , 2013 .

[22]  Li Shen,et al.  A Semisupervised Latent Dirichlet Allocation Model for Object-Based Classification of VHR Panchromatic Satellite Images , 2014, IEEE Geoscience and Remote Sensing Letters.