Change Detection and Image Time-Series Analysis 2 - Supervised Methods (Hardcover)

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.

General

Imprint:	Iste
Country of origin:	United Kingdom
Release date:	2022
First published:	2021
Authors:	A Atto
Dimensions:	248 x 162 x 22mm (L x W x T)
Format:	Hardcover
Pages:	272
ISBN-13:	978-1-78945-057-6
Categories:	Books > Arts & Architecture > Photography & photographs > Photographic equipment & techniques Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision Books > Computing & IT > Applications of computing > Image processing > General Promotions
LSN:	1-78945-057-8
Barcode:	9781789450576

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