Phisical Vulnerability Proxies from Remotes Sensing: Reviewing, Implementing and Disseminating Selected Techniques

作者: Mostapha Mohammad Harb , Daniele De Vecchi , Fabio Dell'Acqua

DOI: 10.1109/MGRS.2015.2398672

关键词: Natural hazardSpatial analysisData miningDisseminationComputer scienceSet (psychology)Vulnerability (computing)Remote sensing (archaeology)Risk managementFeature extraction

摘要: Risk from natural hazards and its direct effect in disrupting human livelihood is of paramount interest for maintaining a sustainable development, remote sensing can be valuable tool collecting relevant information to assess mitigate risk. This paper focuses on monitoring the vulnerability term risk equation, particular physical factor using imagery. It provides an overview state-of-the-art methodologies used extracting set selected indicators. The building defined as probability structural failure extreme situation like quakes. Although very difficult compute precisely, it estimated indirectly through representative indicators called ?proxies?. literature offers different techniques items When optical satellite images are used, both types information, geometrical spectral, useful extract features connected proxies. Therefore, algorithms that combine spectral spatial would more effective choice exploring content acquired data. relationship between aggregation method expected produce time-changing synoptic view. extracted then mapping so support decision-making help optimizing selection management strategy.

参考文章(98)
M.-C. Hung, M. K. Ridd, A SUBPIXEL CLASSIFIER FOR URBAN LAND-COVER MAPPING BASED ON A MAXIMUM-LIKELIHOOD APPROACH AND EXPERT SYSTEM RULES Photogrammetric Engineering and Remote Sensing. ,vol. 68, pp. 1173- 1180 ,(2002)
Harvey J. Miller, Geographic Data Mining and Knowledge Discovery geographic information science. pp. 352- 366 ,(2001) , 10.1002/9780470690819.CH19
Jean Dezert, Albena Tchamova, Pei Wang, On the validity of Dempster-Shafer Theory international conference on information fusion. pp. 655- 660 ,(2012) , 10.5281/ZENODO.22668
D. Amarsaikhan, T. Douglas*, Data fusion and multisource image classification International Journal of Remote Sensing. ,vol. 25, pp. 3529- 3539 ,(2004) , 10.1080/0143116031000115111
John C. Price, Comparing MODIS and ETM+ data for regional and global land classification Remote Sensing of Environment. ,vol. 86, pp. 491- 499 ,(2003) , 10.1016/S0034-4257(03)00127-5