1767998112     

Deep Learning for the Earth Sciences : A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences

Tuia, Devis [Mitwirkende/Mitwirkender] ; Zhu, Xiao Xiang [Mitwirkende/Mitwirkender] ; Reichstein, Markus [Mitwirkende/Mitwirkender]

Newark : John Wiley & Sons, Incorporated, 2021 [©2021]

1 online resource (435 pages)

Englisch

Description based on publisher supplied metadata and other sources.

Erscheint auch als: (Druck-Ausgabe)

978-1-119-64615-0

978-1-119-64614-3 (ISBN der Printausgabe)

Elektronische Ressource: Zugang über Aggregator-Datenbank (Lizenzangabe: Lizenzpflichtig)

Cover -- Title Page -- Copyright -- Contents -- Foreword -- Acknowledgments -- List of Contributors -- List of Acronyms -- Chapter 1 Introduction -- 1.1 A Taxonomy of Deep Learning Approaches -- 1.2 Deep Learning in Remote Sensing -- 1.3 Deep Learning in Geosciences and Climate -- 1.4 Book Structure and Roadmap -- Part I Deep Learning to Extract Information from Remote Sensing Images -- Chapter 2 Learning Unsupervised Feature Representations of Remote Sensing Data with Sparse Convolutional Networks -- 2.1 Introduction -- 2.2 Sparse Unsupervised Convolutional Networks -- 2.2.1 Sparsity as the Guiding Criterion -- 2.2.2 The EPLS Algorithm -- 2.2.3 Remarks -- 2.3 Applications -- 2.3.1 Hyperspectral Image Classification -- 2.3.2 Multisensor Image Fusion -- 2.4 Conclusions -- Chapter 3 Generative Adversarial Networks in the Geosciences -- 3.1 Introduction -- 3.2 Generative Adversarial Networks -- 3.2.1 Unsupervised GANs -- 3.2.2 Conditional GANs -- 3.2.3 Cycle‐consistent GANs -- 3.3 GANs in Remote Sensing and Geosciences -- 3.3.1 GANs in Earth Observation -- 3.3.2 Conditional GANs in Earth Observation -- 3.3.3 CycleGANs in Earth Observation -- 3.4 Applications of GANs in Earth Observation -- 3.4.1 Domain Adaptation Across Satellites -- 3.4.2 Learning to Emulate Earth Systems from Observations -- 3.5 Conclusions and Perspectives -- Chapter 4 Deep Self‐taught Learning in Remote Sensing -- 4.1 Introduction -- 4.2 Sparse Representation -- 4.2.1 Dictionary Learning -- 4.2.2 Self‐taught Learning -- 4.3 Deep Self‐taught Learning -- 4.3.1 Application Example -- 4.3.2 Relation to Deep Neural Networks -- 4.4 Conclusion -- Chapter 5 Deep Learning‐based Semantic Segmentation in Remote Sensing -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Basics on Deep Semantic Segmentation: Computer Vision Models -- 5.3.1 Architectures for Image Data.