Change detection and image time series analysis . 2 : supervised methods / / coordinated by Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone

• Pubblicazione: Hoboken, New Jersey : , : John Wiley & Sons, Incorporated; London, UK : , : ISTE, , [2021]; ©2021
• Descrizione fisica: 1 online resource (288 pages) : illustrations (chiefly colour)
• Disciplina: 621.367
• Soggetto topico: Image analysis
• Persona (resp. second.): AttoAbdourrahmane M. <1974->; BovoloFrancesca; BruzzoneLorenzo
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 1. Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series / Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico and Josiane Zerubia -- ; 2. Pixel-based Classification Techniques for Satellite Image Time Series / Charlotte Pelletier and Silvia Valero -- ; 3. Semantic Analysis of Satellite Image Time Series / Corneliu Octavian Dumitru and Mihai Datcu -- ; 4. Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond / Matthieu Molinier, Jukka Miettinen, Dino Ienco, Shi Qiu and Zhe Zhu -- ; 5. A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images / Gülşen Taşkin, Esra Erten and Enes Oğuzhan Alataş -- ; 6. Multiclass Multilabel Change of State Transfer Learning from Image Time Series / Abdourrahmane M. Atto, Héla Hadhri, Flavien Vernier and Emmanuel Trouvé.
• Sommario/riassunto: "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.
• Titolo autorizzato: Change detection and image time series analysis
• ISBN: 9781119882282; 1119882281; 1-119-88229-X; 1-119-88227-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910829874203321
• Lo trovi qui: Univ. Federico II