An approach for detection of buildings and changes in buildings using orthophotos and point clouds: A case study of Van Erriş earthquake

Abstract This paper presents an image analysis of the Van Erciş earthquake, and demonstrates how efficiently the orthophoto images and point clouds from stereo matching data can be used for automatic detection of buildings and changes in buildings. The proposed method contains three basic steps. The first step is to classify the high-resolution pre and post event Red- Green-Blue (RGB) orthophoto images (orthoRGB) using Support Vector Machine (SVM) classification procedure to extract the building areas. In the second step, normalized Digital Surface Model (nDSM) band derived from point clouds and Digital Terrain Model (DTM) is integrated with the SVM classification (nDSM+orthoRGB). In the last step, building damage assessment is performed through a comparison between two independent classification results from pre-and post-event data. It was observed that using the nDSM band in the classification process as additional bands the accuracy of classification increases significantly.

[1]  G. Sarp.  Determination of Vegetation Change Using Thematic Mapper Imagery in Afşin-Elbistan Lignite Basin; SE Turkey , 2012 .

[2]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[3]  Jefferey A. Shufelt,et al.  Fusion of monocular cues to detect man-made structures in aerial imagery , 1993 .

[4]  Valerio Baiocchi,et al.  Automatic three-dimensional features extraction: The case study of L'Aquila for collapse identification after April 06, 2009 earthquake , 2014 .

[5]  Arzu Erener,et al.  Unsupervised building detection in complex urban environments from multispectral satellite imagery , 2012 .

[6]  Giles M. Foody,et al.  Mapping a specific class with an ensemble of classifiers , 2007 .

[7]  Giles M. Foody,et al.  The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM , 2006 .

[8]  A. Abraham,et al.  Intrusion Detection Systems Using Decision Trees and Support Vector Machines , 2004 .

[9]  Lorenzo Bruzzone,et al.  A multilevel hierarchical approach to classification of high spatial resolution images with support vector machines , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[10]  Valerio Baiocchi,et al.  Development of a software to optimize and plan the acquisitions from UAV and a first application in a post-seismic environment , 2014 .

[11]  M. Turker,et al.  AN INTEGRATED EARTHQUAKE DAMAGE DETECTION SYSTEM , 2006 .

[12]  P. Gamba,et al.  GIs and Image Understanding for Near-Real- Time Earthquake Damage Assessment , 1998 .

[13]  Cheng Gang,et al.  Earthquake-collapsed building extraction from LiDAR and aerophotograph based on OBIA , 2010, The 2nd International Conference on Information Science and Engineering.

[14]  A. Erener,et al.  Evaluating land use/cover change with temporal satellite data and information systems , 2012 .

[15]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[16]  Stefan Voigt,et al.  Satellite Image Analysis for Disaster and Crisis-Management Support , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Francesca Romana Cinti,et al.  Co-seismic surface effects from very high resolution panchromatic images: the case of the 2005 Kashmir (Pakistan) earthquake , 2011 .

[18]  Mustafa Turker,et al.  SPOT HRV data analysis for detecting earthquake-induced changes in Izmit, Turkey , 2003 .

[19]  Haiyan Gu,et al.  Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine , 2010 .

[20]  Nazzareno Pierdicca,et al.  Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies , 2006 .

[21]  A. Erener,et al.  Monitoring Coastline Change Using Remote Sensing and GIS Technologies , 2012 .

[22]  Xin Huang,et al.  A multiscale feature fusion approach for classification of very high resolution satellite imagery based on wavelet transform , 2008 .

[23]  Gustavo Camps-Valls,et al.  Multisource Composite Kernels for Urban-Image Classification , 2010, IEEE Geoscience and Remote Sensing Letters.

[24]  Ajayi Adebowale.  Comparative Study of Selected Data Mining Algorithms Used For Intrusion Detection , 2013 .

[25]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[26]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[27]  Edward R. Dougherty,et al.  Morphological methods in image and signal processing , 1988 .

[28]  Pol Coppin,et al.  Review ArticleDigital change detection methods in ecosystem monitoring: a review , 2004 .

[29]  Eva Savina Malinverni.  Change Detection Applying Landscape Metrics on High Remote Sensing Images , 2011 .

[30]  Yeshwantrao Chavan,et al.  Combining Naive Bayesian and Support Vector Machine for Intrusion Detection System , 2014 .

[31]  Mustafa Turker,et al.  Detection of collapsed buildings caused by the 1999 Izmit, Turkey earthquake through digital analysis of post-event aerial photographs , 2004 .

[32]  George Vosselman,et al.  Two algorithms for extracting building models from raw laser altimetry data , 1999 .

[33]  Valerio Baiocchi,et al.  Three-dimensional multispectral classification and its application to early seismic damage assessment , 2010 .

[34]  Zhang Li,et al.  AUTOMATIC DSM GENERATION FROM LINEAR ARRAY IMAGERY DATA , 2004 .

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

[36]  Naci Yastikli,et al.  Contribution of Normalized DSM to Automatic Building Extraction from HR Mono Optical Satellite Imagery , 2014 .

[37]  Zhang Jing.  Change detection of remote sensing image for earthquake damaged buildings and its application in seismic disaster assessment , 2002 .

[38]  Si Qiao,et al.  Audience Perception of Computer Generated Human Facial Behaviour , 2011, CGAMES 2011.