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