http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Deep learning for multi-sensor Earth observation
1 online resource (xv, 436 pages) : illustrations (some color), maps (some color).
Deep Learning for Multi-Sensor Earth Observation addresses the need for transformative Deep Learning techniques to navigate the complexity of multi-sensor data fusion. With insights drawn from the frontiers of remote sensing technology and AI advancements, it covers the potential of fusing data of varying spatial, spectral, and temporal dimensions from both active and passive sensors. This book offers a concise, yet comprehensive, resource, addressing the challenges of data integration and uncertainty quantification from foundational concepts to advanced applications. Case studies illustrate the practicality of deep learning techniques, while cutting-edge approaches such as self-supervised learning, graph neural networks, and foundation models chart a course for future development. Structured for clarity, the book builds upon its own concepts, leading readers through introductory explanations, sensor-specific insights, and ultimately to advanced concepts and specialized applications. By bridging the gap between theory and practice, this volume equips researchers, geoscientists, and enthusiasts with the knowledge to reshape Earth observation through the dynamic lens of deep learning.
Front Cover -- Deep Learning for Multi-Sensor Earth Observation -- Copyright -- Contents -- Contributors -- Preface -- Acknowledgments -- I Introduction to multi-sensor data and artificial intelligence -- 1 Deep learning for multi-sensor Earth observation: introductory notes -- 1.1 Introduction -- 1.2 Different sensors and modalities -- 1.3 Benefits of fusion -- 1.4 Traditional multi-sensor fusion -- 1.5 Emergence of deep learning -- 1.6 Motivation and contents of the book -- References -- 2 A basic introduction to deep learning -- 2.1 Introduction -- 2.2 Different learning paradigms
2.3 CNN -- 2.4 Image classification -- 2.4.1 LeNet-5 -- 2.4.2 AlexNet -- 2.4.3 VGGNet -- 2.4.4 InceptionNet -- 2.4.5 ResNet -- 2.4.6 DenseNet -- 2.4.7 MobileNet -- 2.5 Semantic segmentation -- 2.6 Target detection -- 2.7 From attention to transformers -- 2.8 Autoencoder and generative models -- 2.9 Practices and tricks -- 2.9.1 Appropriate learning task -- 2.9.2 Data quality -- 2.9.3 Choice of appropriate model -- 2.9.4 Hyperparameter tuning -- 2.9.5 Regularization -- 2.9.6 Loss functions -- 2.10 Conclusion -- References -- II Artificial intelligence for sensor-specific data analysis and fusion
3 Deep learning processing of remotely sensed multi-spectral images -- 3.1 Introduction -- 3.1.1 Remotely sensed multi-spectral images -- 3.1.2 Deep learning methods taxonomy -- 3.1.3 Chapter overview -- 3.2 Image preprocessing techniques -- 3.2.1 Super-resolution -- 3.2.2 Image fusion -- 3.2.2.1 Homogeneous fusion -- 3.2.2.2 Spatiotemporal fusion -- 3.2.2.3 Heterogeneous fusion -- 3.2.3 Segmentation -- 3.2.4 Image registration and enhancement -- 3.2.4.1 Registration -- 3.2.4.2 Denoising -- 3.3 Image analysis -- 3.3.1 Change detection -- 3.3.2 Object detection and recognition
3.3.3 Scene classification -- 3.4 Conclusions and perspectives -- Acknowledgments -- References -- 4 Deep learning and hyperspectral images -- 4.1 Introduction -- 4.2 Image classification -- 4.2.1 Limited label learning for HSI classification -- 4.2.1.1 Self-supervised learning for HSI classification -- 4.2.1.2 Semi-supervised learning for HSI classification -- 4.2.1.3 Meta learning for HSI classification -- 4.3 Dimensionality reduction -- 4.3.1 Band selection -- 4.3.2 Feature extraction -- 4.4 Unmixing -- 4.5 Image enhancement -- 4.5.1 Denoising -- 4.5.2 Super-resolution -- 4.5.3 Inpainting
4.6 Change detection -- 4.7 Future direction -- 4.8 Summary -- References -- 5 Synthetic aperture radar image analysis in era of deep learning -- 5.1 Introduction -- 5.2 SAR despeckling -- 5.3 SAR image classification -- 5.4 SAR semantic segmentation -- 5.5 SAR target detection -- 5.6 SAR domain adaptation -- 5.7 SAR-optical transcoding -- 5.8 PolSAR image analysis -- 5.9 InSAR data analysis -- 5.10 SAR analysis benefits from other sensors -- 5.11 Conclusion -- References -- 6 Deep learning with lidar for Earth observation -- 6.1 Introduction
Includes bibliographical references and index.