Change Detection and Image Time Series Analysis 2

Supervised Methods

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Wiley-ISTE


Paru le : 2021-12-01



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Description
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.
Pages
272 pages
Collection
n.c
Parution
2021-12-01
Marque
Wiley-ISTE
EAN papier
9781789450576
EAN PDF
9781119882275

Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
272
Taille du fichier
17367 Ko
Prix
163,47 €
EAN EPUB
9781119882282

Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
272
Taille du fichier
4106 Ko
Prix
163,47 €

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