Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data

ABSTRACT Building damage maps after disasters can help us to better manage the rescue operations. Researchers have used Light Detection and Ranging (LiDAR) data for extracting the building damage maps. For producing building damage maps from LiDAR data in a rapid manner, it is necessary to understand the effectiveness of features and classifiers. However, there is no comprehensive study on the performance of features and classifiers in identifying damaged areas. In this study, the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated. In the proposed method, at first, a pre-processing stage was utilized to apply essential processes on post-event LiDAR data. Second, textural features were extracted from the pre-processed LiDAR data. Third, fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents. The proposed method was tested across three areas over the 2010 Haiti earthquake. Three building damage maps with overall accuracies of 75.0%, 78.1% and 61.4% were achieved. Based on outcomes, the fuzzy inference systems were stronger than random forest, bagging, boosting and support vector machine classifiers for detecting damaged buildings.

[1]  Hannes Taubenböck,et al.  Remote sensing contributing to assess earthquake risk: from a literature review towards a roadmap , 2013, Natural Hazards.

[2]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[3]  Sergios Theodoridis,et al.  Chapter 7 – Feature Generation II , 2006 .

[4]  D. Hölbling,et al.  Automated Damage Indication for Rapid Geospatial Reporting , 2011 .

[5]  J. Shan,et al.  Building Extraction and Rubble Mapping for City Port-au-Prince Post-2010 Earthquake with GeoEye-1 Imagery and Lidar Data , 2011 .

[6]  Jianbo Liu,et al.  An Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing , 2016, Remote. Sens..

[7]  Qingquan Li,et al.  A Novel 3D Building Damage Detection Method Using Multiple Overlapping UAV Images , 2014 .

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

[9]  Timo Balz,et al.  Building-damage detection using post-seismic high-resolution SAR satellite data , 2010 .

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

[11]  J. Shan,et al.  Topographic laser ranging and scanning : principles and processing , 2008 .

[12]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[13]  W. K. Lam,et al.  Classification of rotated and scaled textures by local linear operators , 1995, Proceedings of ISCAS'95 - International Symposium on Circuits and Systems.

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

[15]  J. Niemeyer,et al.  Contextual classification of lidar data and building object detection in urban areas , 2014 .

[16]  Ali Mohammadzadeh,et al.  Building Damage Detection Using Object-Based Image Analysis and ANFIS From High-Resolution Image (Case Study: BAM Earthquake, Iran) , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Serge Guillaume,et al.  Designing fuzzy inference systems from data: An interpretability-oriented review , 2001, IEEE Trans. Fuzzy Syst..

[18]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[19]  Jie Shan and Charles K. Toth.,et al.  Topographic laser ranging and scanning , 2017 .

[20]  C. Unsalan,et al.  Building detection from aerial images using invariant color features and shadow information , 2008, 2008 23rd International Symposium on Computer and Information Sciences.

[21]  Masashi Matsuoka,et al.  Detection and Animation of Damage using very High-Resolution Satellite Data following the 2003 Bam, Iran, Earthquake , 2005 .

[22]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[23]  M. Chantler.  The effect of variation in illuminant direction on texture classification , 1994 .

[24]  P. Reinartz,et al.  Building damage assessment after the earthquake in Haiti using two post-event satellite stereo imagery and DSMs , 2013, Joint Urban Remote Sensing Event 2013.

[25]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[26]  R. A. Rahmat,et al.  GENERATION OF FUZZY RULES WITH SUBTRACTIVE CLUSTERING , 2005 .

[27]  Stuart P. D. Gill,et al.  A Comprehensive Analysis of Building Damage in the 12 January 2010 Mw7 Haiti Earthquake Using High-Resolution Satellite and Aerial Imagery , 2011 .

[28]  Lucien Wald,et al.  Urban damage assessment using multimodal QuickBird images and ancillary data: the Bam and the Boumerdes earthquakes , 2008 .

[29]  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 .

[30]  Mohammad Taleai,et al.  Building change detection after earthquake using multi-criteria decision analysis based on extracted information from high spatial resolution satellite images , 2017 .

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

[32]  Ali Mohammadzadeh,et al.  A Fuzzy-GA Based Decision Making System for Detecting Damaged Buildings from High-Spatial Resolution Optical Images , 2017, Remote. Sens..

[33]  Wei Liu,et al.  Evaluation of Three-Dimensional Shape Signatures for Automated Assessment of Post-Earthquake Building Damage , 2013 .

[34]  F. Dell’acqua,et al.  Post-event Only VHR Radar Satellite Data for Automated Damage Assessment , 2011 .

[35]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[36]  Paolo Gamba,et al.  Earthquake damage assessment from post-event VHR radar data: From Sichuan, 2008 to Haiti, 2010 , 2011, 2011 Joint Urban Remote Sensing Event.

[37]  Huadong Guo,et al.  A framework for automated assessment of post-earthquake building damage using geospatial data , 2012 .

[38]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

[39]  Chunlin Huang,et al.  Building Earthquake Damage Information Extraction from a Single Post-Earthquake PolSAR Image , 2016, Remote. Sens..

[40]  S. J. Oude Elberink,et al.  Role of dimensionality reduction in segment - based classsification of damaged building roofs in ariborne laser scanning data , 2012 .

[41]  Mohammad Taleai,et al.  Détection de dommages et évaluation des dégâts du réseau routier après un séisme, en utilisant des images QuickBird haute résolution , 2010 .

[42]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[43]  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.

[44]  Christiane Weber,et al.  Towards a rapid automatic detection of building damage using remote sensing for disaster management : The 2010 Haiti earthquake , 2014 .

[45]  Lingli Zhao,et al.  Damage assessment in urban areas using post-earthquake airborne PolSAR imagery , 2013 .

[46]  Sergios Theodoridis,et al.  Feature Generation II , 2009 .

[47]  Dmitry Bespalov,et al.  Automated method for detection and quantification of building damage and debris using post-disaster lidar data , 2011, Defense + Commercial Sensing.

[48]  Sudan Xu,et al.  Segment-Based Classification of Damaged Building Roofs in Aerial Laser Scanning Data , 2013, IEEE Geoscience and Remote Sensing Letters.

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

[50]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[51]  Masashi Matsuoka,et al.  Use of Satellite SAR Intensity Imagery for Detecting Building Areas Damaged Due to Earthquakes , 2004 .

[52]  S. O. Elberink,et al.  Detection of collapsed buildings by classifying segmented lidar data: ISPRS Calgary Workshop, held on 29-31 August 2011, Calgary, Canada , 2011 .

[53]  R. Pontius,et al.  Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment , 2011 .

[54]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[55]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[56]  Cem Ünsalan,et al.  Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[57]  Paolo Gamba,et al.  Experiences in optical and SAR imagery analysis for damage assessment in the Wuhan, may 2008 earthquake , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[58]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[59]  Kaiguang Zhao,et al.  Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues , 2010, Remote. Sens..

[60]  Hans-Peter Bähr,et al.  DETECTION AND ANALYSIS OF BUILDING DAMAGE CAUSED BY EARTHQUAKES USING LASER SCANNING DATA , 2007 .

[61]  Richard Lepage,et al.  Fast and Efficient Evaluation of Building Damage From Very High Resolution Optical Satellite Images , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[62]  Paolo Gamba,et al.  Fast damage mapping in case of earthquakes using multitemporal SAR data , 2008, Journal of Real-Time Image Processing.

[63]  S. J. Oude Elberink,et al.  Detection of collapsed buildings by classifying segmented airborne laser scanner data. , 2012 .

[64]  Wai Yeung Yan,et al.  Urban land cover classification using airborne LiDAR data: A review , 2015 .

[65]  Farhad Samadzadegan,et al.  A fuzzy decision making system for building damage map creation using high resolution satellite imagery , 2013 .

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

[67]  Shanlin Yang,et al.  Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation , 2014, Science China Information Sciences.