LEADER 03974nam 2200529 450 001 9910829874203321 005 20231206232350.0 010 $a9781119882282 010 $a1119882281 010 $a1-119-88229-X 010 $a1-119-88227-3 024 7 $a10.1002/9781119882299 035 $a(OCoLC)1310455010 035 $a(MiAaPQ)EBC6817982 035 $a(EXLCZ)9919935015900041 100 $a20220819h20212021 uy 0 101 0 $aeng 135 $auran#|||mna|a 181 $ctxt$2rdacontent 181 $csti$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aChange detection and image time series analysis$h2 $esupervised methods /$fcoordinated by Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons, Incorporated ;$aLondon, UK :$cISTE,$d[2021] 210 4$d©2021 215 $a1 online resource (288 pages) $cillustrations (chiefly colour) 311 08$aPrint version: Atto, Abdourrahmane M. Change Detection and Image Time Series Analysis 2 Newark : John Wiley & Sons, Incorporated,c2022 9781789450576 320 $aIncludes bibliographical references and index. 327 $g1.$tHierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series /$rIhsen Hedhli, Gabriele Moser, Sebastiano B. Serpico and Josiane Zerubia --$g2.$tPixel-based Classification Techniques for Satellite Image Time Series /$rCharlotte Pelletier and Silvia Valero --$g3.$tSemantic Analysis of Satellite Image Time Series /$rCorneliu Octavian Dumitru and Mihai Datcu --$g4.$tOptical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond /$rMatthieu Molinier, Jukka Miettinen, Dino Ienco, Shi Qiu and Zhe Zhu --$g5.$tA Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images /$rGül?en Ta?kin, Esra Erten and Enes O?uzhan Alata? --$g6.$tMulticlass Multilabel Change of State Transfer Learning from Image Time Series /$rAbdourrahmane M. Atto, Héla Hadhri, Flavien Vernier and Emmanuel Trouvé. 330 $a"Change Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series.Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches.Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns.Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations,Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues."--Provided by publisher. 606 $aImage analysis$vCongresses 615 0$aImage analysis 676 $a621.367 702 $aAtto$b Abdourrahmane M.$f1974- 702 $aBovolo$b Francesca 702 $aBruzzone$b Lorenzo 801 0$bDG1 801 1$bDG1 801 2$bOCLCF 801 2$bOCLCO 801 2$bOCLCQ 801 2$bCSt 801 2$bMiAaPQ 801 2$bCaOWtU 912 $a9910829874203321 996 $aChange detection and image time series analysis$94045978 997 $aUNINA