Deep learning for the earth sciences : a comprehensive approach to remote sensing, climate science and geosciences /: a comprehensive approach to remote sensing, climate science and geosciences. (2021)

• Record Type: Book
• Further Information: Note: Edited by Gustau Camps-Valls, Devis Tuia, Xiao Xiang Zhu, Markus Reichstein.
• Editors: Camps-Valls, Gustau; Tuia, Devis; Zhu, Xiao Xiang; Reichstein, Markus
• Contents: Foreword xvii Acknowledgments xix List of Contributors xxi List of Acronyms xxvii 1 Introduction 1; Gustau Camps-Valls, Xiao Xiang Zhu, Devis Tuia, and Markus Reichstein 1.1 A Taxonomy of Deep Learning Approaches 2 1.2 Deep Learning in Remote Sensing 3 1.3 Deep Learning in Geosciences and Climate 7 1.4 Book Structure and Roadmap 9 Part I Deep Learning to Extract Information from Remote Sensing Images 13 2 Learning Unsupervised Feature Representations of Remote Sensing Data with Sparse Convolutional Networks 15; Jose E. Adsuara, Manuel Campos-Taberner, Javier García-Haro, Carlo Gatta, Adriana Romero, and Gustau Camps-Valls 2.1 Introduction 15 2.2 Sparse Unsupervised Convolutional Networks 17 2.2.1 Sparsity as the Guiding Criterion 17 2.2.2 The EPLS Algorithm 18 2.2.3 Remarks 18 2.3 Applications 19 2.3.1 Hyperspectral Image Classification 19 2.3.2 Multisensor Image Fusion 21 2.4 Conclusions 22 3 Generative Adversarial Networks in the Geosciences 24; Gonzalo Mateo-García, Valero Laparra, Christian Requena-Mesa, and Luis Gómez-Chova 3.1 Introduction 24 3.2 Generative Adversarial Networks 25 3.2.1 Unsupervised GANs 25 3.2.2 Conditional GANs 26 3.2.3 Cycle-consistent GANs 27 3.3 GANs in Remote Sensing and Geosciences 28 3.3.1 GANs in Earth Observation 28 3.3.2 Conditional GANs in Earth Observation 30 3.3.3 CycleGANs in Earth Observation 30 3.4 Applications of GANs in Earth Observation 31 3.4.1 Domain Adaptation Across Satellites 31 3.4.2 Learning to Emulate Earth Systems from Observations 33 3.5 Conclusions and Perspectives 36 4 Deep Self-taught Learning in Remote Sensing 37; Ribana Roscher 4.1 Introduction 37 4.2 Sparse Representation 38 4.2.1 Dictionary Learning 39 4.2.2 Self-taught Learning 40 4.3 Deep Self-taught Learning 40 4.3.1 Application Example 43 4.3.2 Relation to Deep Neural Networks 44 4.4 Conclusion 45 5 Deep Learning-based Semantic Segmentation in Remote Sensing 46; Devis Tuia, Diego Marcos, Konrad Schindler, and Bertrand Le Saux 5.1 Introduction 46 5.2 Literature Review 47 5.3 Basics on Deep Semantic Segmentation: Computer Vision Models 49 5.3.1 Architectures for Image Data 49 5.3.2 Architectures for Point-clouds 52 5.4 Selected Examples 55 5.4.1 Encoding Invariances to Train Smaller Models: The example of Rotation 55 5.4.2 Processing 3D Point Clouds as a Bundle of Images: SnapNet 59 5.4.3 Lake Ice Detection from Earth and from Space 62 5.5 Concluding Remarks 66 6 Object Detection in Remote Sensing 67; Jian Ding, Jinwang Wang, Wen Yang, and Gui-Song Xia 6.1 Introduction 67 6.1.1 Problem Description 67 6.1.2 Problem Settings of Object Detection 69 6.1.3 Object Representation in Remote Sensing 69 6.1.4 Evaluation Metrics 69 6.1.4.1 Precision-recall Curve 70 6.1.4.2 Average Precision and Mean Average Precision 71 6.1.5 Applications 71 6.2 Preliminaries on Object Detection with Deep Models 72 6.2.1 Two-stage Algorithms 72 6.2.1.1 R-CNNs 72 6.2.1.2 R-FCN 73 6.2.2 One-stage Algorithms 73 6.2.2.1 YOLO 73 6.2.2.2 SSD 73 6.3 Object Detection in Optical RS Images 75 6.3.1 RelatedWorks 75 6.3.1.1 Scale Variance 75 6.3.1.2 Orientation Variance 75 6.3.1.3 Oriented Object Detection 75 6.3.1.4 Detecting in Large-size Images 76 6.3.2 Datasets and Benchmark 77 6.3.2.1 DOTA 77 6.3.2.2 VisDrone 77 6.3.2.3 DIOR 77 6.3.2.4 xView 77 6.3.3 Two Representative Object Detectors in Optical RS Images 78 6.3.3.1 Mask OBB 78 6.3.3.2 RoI Transformer 82 6.4 Object Detection in SAR Images 86 6.4.1 Challenges of Detection in SAR Images 86 6.4.2 RelatedWorks 86 6.4.3 Datasets and Benchmarks 88 6.5 Conclusion 89 7 Deep Domain adaptation in Earth Observation 90; Benjamin Kellenberger, Onur Tasar, Bharath Bhushan Damodaran, Nicolas Courty, and Devis Tuia 7.1 Introduction 90 7.2 Families of Methodologies 91 7.3 Selected Examples 93 7.3.1 Adapting the Inner Representation 93 7.3.2 Adapting the Inputs Distribution 97 7.3.3 Using (few, well chosen) Labels from the Target Domain 100 7.4 Concluding remarks 104 8 Recurrent Neural Networks and the Temporal Component 105; Marco Körner and Marc Rußwurm 8.1 Recurrent Neural Networks 106 8.1.1 Training RNNs 107 8.1.1.1 Exploding and Vanishing Gradients 107 8.1.1.2 Circumventing Exploding and Vanishing Gradients 109 8.2 Gated Variants of RNNs 111 8.2.1 Long Short-term Memory Networks 111 8.2.1.1 The Cell State c t and the Hidden State h t 112 8.2.1.2 The Forget Gate f t 112 8.2.1.3 The Modulation Gate v t and the Input Gate i t 112 8.2.1.4 The Output Gate o t 112 8.2.1.5 Training LSTM Networks 113 8.2.2 Other Gated Variants 113 8.3 Representative Capabilities of Recurrent Networks 114 8.3.1 Recurrent Neural Network Topologies 114 8.3.2 Experiments 115 8.4 Application in Earth Sciences 117 8.5 Conclusion 118 9 Deep Learning for Image Matching and Co-registration 120; Maria Vakalopoulou, Stergios Christodoulidis, Mihir Sahasrabudhe, and Nikos Paragios 9.1 Introduction 120 9.2 Literature Review 123 9.2.1 Classical Approaches 123 9.2.2 Deep Learning Techniques for Image Matching 124 9.2.3 Deep Learning Techniques for Image Registration 125 9.3 Image Registration with Deep Learning 126 9.3.1 2D Linear and Deformable Transformer 126 9.3.2 Network Architectures 127 9.3.3 Optimization Strategy 128 9.3.4 Dataset and Implementation Details 129 9.3.5 Experimental Results 129 9.4 Conclusion and Future Research 134 9.4.1 Challenges and Opportunities 134 9.4.1.1 Dataset with Annotations 134 9.4.1.2 Dimensionality of Data 135 9.4.1.3 Multitemporal Datasets 135 9.4.1.4 Robustness to Changed Areas 135 10 Multisource Remote Sensing Image Fusion 136; Wei He, Danfeng Hong, Giuseppe Scarpa, Tatsumi Uezato, and Naoto Yokoya 10.1 Introduction 136 10.2 Pansharpening 137 10.2.1 Survey of Pansharpening Methods Employing Deep Learning 137 10.2.2 Experimental Results 140 10.2.2.1 Experimental Design 140 10.2.2.2 Visual and Quantitative Comparison in Pansharpening 140 10.3 Multiband Image Fusion 143 10.3.1 Supervised Deep Learning-based Approaches 143 10.3.2 Unsupervised Deep Learning-based Approaches 145 10.3.3 Experimental Results 146 10.3.3.1 Comparison Methods and Evaluation Measures 146 10.3.3.2 Dataset and Experimental Setting 146 10.3.3.3 Quantitative Comparison a
• Edition: 1st
• Publisher Details: Hoboken : John Wiley & Sons, Inc
• Publication Date: 2021
• Extent: 1 online resource
• Subjects: 550.285631; Earth sciences -- Data processing; Machine learning
• Languages: English
• ISBNs: 9781119646167
• Related ISBNs: 9781119646143
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
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