Remote Sensing Of Water Resources Disasters And Urban Studies
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Remote Sensing of Water Resources, Disasters, and Urban Studies
Author: Ph.D., Prasad S. Thenkabail
language: en
Publisher: CRC Press
Release Date: 2015-10-02
This book is the most comprehensive documentation of the scientific and methodological advances that have taken place in understanding remote sensing data, methods, and applications over last 50 years. In a very practical way it demonstrates the experience, utility, methods and models used in studying a wide array of water applications. There are more than 100 leading global experts in the field contributing to this work.
Remote Sensing Handbook - Three Volume Set
A volume in the three-volume Remote Sensing Handbook series, Remote Sensing of Water Resources, Disasters, and Urban Studies documents the scientific and methodological advances that have taken place during the last 50 years. The other two volumes in the series are Remotely Sensed Data Characterization, Classification, and Accuracies, and Land Reso
Change Detection and Image Time Series Analysis 2
Author: Abdourrahmane M. Atto
language: en
Publisher: John Wiley & Sons
Release Date: 2021-12-29
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.