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.
1. Part I: Introduction to multi-sensor data and artificial intelligence - p. 1 2. Chapter 1. Deep learning for multi-sensor Earth observation: introductory notes - p. 3 3. Chapter 2. A basic introduction to deep learning - p. 19 4. Part II: Artificial intelligence for sensor-specific data analysis and fusion - p. 55 5. Chapter 3. Deep learning processing of remotely sensed multi-spectral images - p. 57 6. Chapter 4. Deep learning and hyperspectral images - p. 87 7. Chapter 5. Synthetic aperture radar image analysis in era of deep learning - p. 125 8. Chapter 6. Deep learning with lidar for Earth observation - p. 141 9. Chapter 7. Several sensors and modalities - p. 163 10. Part III: Advanced concepts and architectures - p. 179 11. Chapter 8. Self-supervised learning for multi-modal Earth observation data - p. 181 12. Chapter 9. Vision transformers and multi-sensor Earth observation - p. 201 13. Chapter 10. Graph neural networks for multi-sensor Earth observation - p. 211 14. Chapter 11. Uncertainty quantification in deep neural networks for multi-sensor Earth observation - p. 231 15. Part IV: Multi-sensor deep learning applications - p. 249 16. Chapter 12. Multi-sensor deep learning for change detection - p. 251 17. Chapter 13. Multi-sensor deep learning for glacier mapping - p. 287 18. Chapter 14. Deep learning in multi-sensor agriculture and crop management - p. 335 19. Chapter 15. Miscellaneous applications of deep learning based multi-sensor Earth observation - p. 381 20. Chapter 16. Multi-sensor Earth observation: outlook - p. 409 21. Index - p. 425
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.
1. Part I: Introduction to multi-sensor data and artificial intelligence - p. 1 2. Chapter 1. Deep learning for multi-sensor Earth observation: introductory notes - p. 3 3. Chapter 2. A basic introduction to deep learning - p. 19 4. Part II: Artificial intelligence for sensor-specific data analysis and fusion - p. 55 5. Chapter 3. Deep learning processing of remotely sensed multi-spectral images - p. 57 6. Chapter 4. Deep learning and hyperspectral images - p. 87 7. Chapter 5. Synthetic aperture radar image analysis in era of deep learning - p. 125 8. Chapter 6. Deep learning with lidar for Earth observation - p. 141 9. Chapter 7. Several sensors and modalities - p. 163 10. Part III: Advanced concepts and architectures - p. 179 11. Chapter 8. Self-supervised learning for multi-modal Earth observation data - p. 181 12. Chapter 9. Vision transformers and multi-sensor Earth observation - p. 201 13. Chapter 10. Graph neural networks for multi-sensor Earth observation - p. 211 14. Chapter 11. Uncertainty quantification in deep neural networks for multi-sensor Earth observation - p. 231 15. Part IV: Multi-sensor deep learning applications - p. 249 16. Chapter 12. Multi-sensor deep learning for change detection - p. 251 17. Chapter 13. Multi-sensor deep learning for glacier mapping - p. 287 18. Chapter 14. Deep learning in multi-sensor agriculture and crop management - p. 335 19. Chapter 15. Miscellaneous applications of deep learning based multi-sensor Earth observation - p. 381 20. Chapter 16. Multi-sensor Earth observation: outlook - p. 409 21. Index - p. 425
