Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

References

• Abarca-Hernández F, Chica-Olmo M. 1999. Evaluation of geostatistical measures of radiometric spatial variability for lithologic discrimination in Landsat TM images. Photogramm Eng Remote Sensing. 65:705–711.
   Web of Science ®Google Scholar
• Al-Khudhairy DHA, Caravaggi I, Glada S. 2005. Structural damage assessments from Ikonos data using change detection, object-oriented segmentation, and classification techniques. Photogramm Eng Remote Sensing. 71:825.
   Web of Science ®Google Scholar
• Barnes CF, Fritz H, Yoo J. 2007. Hurricane disaster assessments with image-driven data mining in high-resolution satellite imagery. IEEE Trans Geosci Remote Sensing. 45:1631–1640.
   Web of Science ®Google Scholar
• Battersby SE, Hodgson ME, Wang J. 2012. Spatial resolution imagery requirements for identifying structure damage in a hurricane disaster: a cognitive approach. Photogramm Eng Remote Sensing. 78:625–635.
   Web of Science ®Google Scholar
• Bourgault G, Marcotte D. 1991. Multivariable variogram and its application to the linear model of coregionalization. Math Geol. 23:899–928.
   Google Scholar
• Butenuth M, Frey D, Nielsen AA, Skriver H. 2011. Infrastructure assessment for disaster management using multi-sensor and multi-temporal remote sensing imagery. Int J Remote Sensing. 32:8575–8594.
   Web of Science ®Google Scholar
• Chen D, Stow DA, Gong P. 2004. Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case. Int J Remote Sensing. 25:2177–2192.
   Web of Science ®Google Scholar
• Chen Z, Hutchinson TC. 2007. Urban damage estimation using statistical processing of satellite images. J Comput Civil Eng. 21:187–199.
   Web of Science ®Google Scholar
• Chen Z, Hutchinson TC. 2010. Probabilistic urban structural damage classification using bitemporal satellite images. Earthquake Spectra. 26:87–109.
   Web of Science ®Google Scholar
• Chica-Olmo M, Abarca-Hernandez F. 2000. Computing geostatistical image texture for remotely sensed data classification. Comput Geosci. 26:373–383.
   Web of Science ®Google Scholar
• Chih-Wei H, Chih-Jen L. 2002. A comparison of methods for multiclass support vector machines. IEEE Trans Neural Networks. 13:415–425.
   PubMed Web of Science ®Google Scholar
• Ehlers M, Gähler M, Janowsky R. 2003. Automated analysis of ultra high resolution remote sensing data for biotope type mapping: new possibilities and challenges. ISPRS J Photogramm Remote Sensing. 57:315–326.
   Web of Science ®Google Scholar
• Friedland CJ, Adams BJ, Levitan ML. 2007a. Remote sensing classification of hurricane storm surge structural damage. ASCE Structures Congress 2007. Long Beach (CA): American Society of Civil Engineers.
   Google Scholar
• Friedland CJ, Adams BJ, Levitan ML. 2007b. Results of neighborhood level analysis of structural storm surge damage to residential structures. Fifth International Workshop on Remote Sensing for Post-Disaster Response; 2007 Sep 10–12; Washington, DC.
   Google Scholar
• Friedland CJ, Massarra CC, Henderson E. 2011. Integrated aerial-based and ground-based damage assessment of single family dwellings at the neighborhood and per-building spatial scales. 9th International Workshop on Remote Sensing for Disaster Response; Stanford, CA.
   Google Scholar
• Fritz HM, Blount C, Sokoloski R, Singleton J, Fuggle A, McAdoo BG, Moore A, Grass C, Tate B. 2007. Hurricane Katrina storm surge distribution and field observations on the Mississippi Barrier Islands. Estuar Coast Shelf Sci. 74:12–20.
   Web of Science ®Google Scholar
• Ghosh S, Adams BJ, Womble JA, Friedland C, Eguchi T. 2007. Deployment of remote sensing technology for multi-hazard post-Katrina damage assessment. Proceedings of the 2nd International Conference on Urban Disaster Reduction; Taipei, Taiwan.
   Google Scholar
• Guo H. 2010. Understanding global natural disasters and the role of earth observation. Int J Digital Earth. 3:221–230.
   Web of Science ®Google Scholar
• Haralick RM, Sternberg SR, Zhuang X. 1987. Image analysis using mathematical morphology. IEEE Trans Pattern Anal Mach Intell. PAMI-9:532–550.
   Web of Science ®Google Scholar
• Hussain E, Ural S, Kim K, Fu C-S, Shan J. 2011. Building extraction and rubble mapping for City Port-au-Prince post-2010 earthquake with GeoEye-1 imagery and Lidar data. Photogramm Eng Remote Sensing. 77:1011–1023.
   Web of Science ®Google Scholar
• Hutchinson TC, Chen ZQ. 2005. Optimized estimated ground truth for object-based urban damage estimation using satellite images from the 2003 Bam, Iran, earthquake. Earthquake Spectra. 21:239–254.
   Google Scholar
• Ill EWR, Chappell DK, Baldwin DG. 1997. AVHRR imagery used to identify hurricane damage in a forested wetland of Louisiana. Photogramm Eng Remote Sensing. 63:293–297.
   Web of Science ®Google Scholar
• Jensen JR. 2007. Remote sensing of the environment: an earth resource perspective 2/e. Upper Saddle River (NJ): Pearson Prentice Hall.
   Google Scholar
• Jin H, Li P, Cheng T, Song B. 2011. Land cover classification using CHRIS/PROBA images and multi-temporal texture. Int J Remote Sensing. 33:101–119.
   Web of Science ®Google Scholar
• Karathanassi V, Iossifidis C, Rokos D. 2000. A texture-based classification method for classifying built areas according to their density. Int J Remote Sensing. 21:1807–1823.
   Web of Science ®Google Scholar
• Kashani AG, Crawford PS, Biswas SK, Graettinger AJ, Grau D. 2014. Automated tornado damage assessment and wind speed estimation based on terrestrial laser scanning. J Comput Civil Eng. [Internet]. [cited 2014 Nov 10];1. DOI: 10.1061/(ASCE)CP.1943-5487.0000389
   Web of Science ®Google Scholar
• Li H, Gu H, Han Y, Yang J. 2010. Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine. Int J Remote Sensing. 31:1453–1470.
   Web of Science ®Google Scholar
• Li M, Cheng L, Gong J, Liu Y, Chen Z, Li F, Chen G, Chen D, Song X. 2008. Post-earthquake assessment of building damage degree using LiDAR data and imagery. Sci China Ser E Technol Sci. 51:133–143.
   Google Scholar
• Li P, Xiao X. 2007. Multispectral image segmentation by a multichannel watershed-based approach. Int J Remote Sensing. 28:4429–4452.
   Web of Science ®Google Scholar
• Li P, Xu H, Song B. 2011. A novel method for urban road damage detection using very high resolution satellite imagery and road map. Photogramm Eng Remote Sensing. 77:1057–1066.
   Web of Science ®Google Scholar
• Li P, Yu H, Cheng T. 2009. Lithologic mapping using ASTER imagery and multivariate texture. Can J Remote Sensing. 35:S117–S125.
   Web of Science ®Google Scholar
• Li Y, van de Lindt JW. 2012. Loss-based formulation for multiple hazards with application to residential buildings. Eng Struct. 38:123–133.
   Web of Science ®Google Scholar
• Liou Y-A, Kar SK, Chang L. 2010. Use of high-resolution FORMOSAT-2 satellite images for post-earthquake disaster assessment: a study following the 12 May 2008 Wenchuan earthquake. Int J Remote Sensing. 31:3355–3368.
   Web of Science ®Google Scholar
• Melesse AM, Jordan JD. 2002. A comparison of fuzzy vs. augmented-ISODATA classification algorithms for cloud–shadow discrimination from Landsat images. Photogramm Eng Remote Sensing. 68:905–912.
   Web of Science ®Google Scholar
• Olsen MJ, Chen Z, Hutchinson T, Kuester F. 2013. Optical techniques for multiscale damage assessment. Geomat Nat Hazards Risk. 4:49–70.
   Web of Science ®Google Scholar
• Pesaresi M, Gerhardinger A, Haag F. 2007. Rapid damage assessment of built-up structures using VHR satellite data in tsunami-affected areas. Int J Remote Sensing. 28:3013–3036.
   Web of Science ®Google Scholar
• Radhika S, Tamura Y, Matsui M. 2012. Use of post-storm images for automated tornado-borne debris path identification using texture-wavelet analysis. J Wind Eng Ind Aerodyn. 107–108:202–213.
   Web of Science ®Google Scholar
• Soille P, Pesaresi M. 2002. Advances in mathematical morphology applied to geoscience and remote sensing. IEEE Trans Geosci Remote Sensing. 40:2042–2055.
   Web of Science ®Google Scholar
• Szantoi Z, Malone S, Escobedo F, Misas O, Smith S, Dewitt B. 2012. A tool for rapid post-hurricane urban tree debris estimates using high resolution aerial imagery. Int J Appl Earth Obs Geoinf. 18:548–556.
   Web of Science ®Google Scholar
• Taskin Kaya G, Musaoglu N, Ersoy OK. 2011. Damage assessment of 2010 Haiti earthquake with post-earthquake satellite image by support vector selection and adaptation. Photogramm Eng Remote Sensing. 77:1025–1035.
   Web of Science ®Google Scholar
• Thompson BK, Escobedo FJ, Staudhammer CL, Matyas CJ, Qiu Y. 2011. Modeling hurricane-caused urban forest debris in Houston, Texas. Landsc Urban Plan. 101:286–297.
   Web of Science ®Google Scholar
• Tralli DM, Blom RG, Zlotnicki V, Donnellan A, Evans DL. 2005. Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards. ISPRS J Photogramm Remote Sensing. 59:185–198.
   Web of Science ®Google Scholar
• Vijayaraj V, Bright EA, Bhaduri BL. 2008. Rapid damage assessment from high resolution imagery. Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International.
   Google Scholar
• Voigt S, Schneiderhan T, Twele A, Gähler M, Stein E, Mehl H. 2011. Rapid damage assessment and situation mapping: learning from the 2010 Haiti earthquake. Photogramm Eng Remote Sensing. 77:923–931.
   Web of Science ®Google Scholar
• Wang F, Xu YJ. 2010. Comparison of remote sensing change detection techniques for assessing hurricane damage to forests. Environ Monit Assess. 162:311–326.
   PubMed Web of Science ®Google Scholar
• Wang W, Qu JJ, Hao X, Liu Y, Stanturf JA. 2010. Post-hurricane forest damage assessment using satellite remote sensing. Agric For Meteorol. 150:122–132.
   Web of Science ®Google Scholar
• Womble J, Ghosh S, Adams B, Friedland C. 2006. Advanced damage detection for Hurricane Katrina: integrating remote sensing and VIEWS field reconnaissance. Buffalo (NY): MCEER.
   Google Scholar
• Womble JA, Adams BJ, Ghosh S, Friedland CJ. 2008. Remote sensing and field reconnaissance for rapid damage detection in Hurricane Katrina. ASCE Structures Congress 2008. Vancouver, Canada: American Society of Civil Engineers.
   Google Scholar
• Yuan F, Sawaya KE, Loeffelholz BC, Bauer ME. 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing. Remote Sensing Environ. 98:317–328.
   Web of Science ®Google Scholar
• Zhang J, Zhou C, Xu K, Watanabe M. 2002. Flood disaster monitoring and evaluation in China. Global Environ Change B Environ Hazards. 4:33–43.
   Google Scholar
• Zheng S, Cao CX, Zhang H, Gao MX, Xu M, Zhao J, Chen W, Li XW. 2011. Assessment of the degree of building damage caused by the 2010 Yushu, China earthquake using satellite and airborne data. Geomat Nat Hazards Risk. 2:141–157.
   Web of Science ®Google Scholar