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.
| ISBN: | 9780443264849 |
| Publication date: | 1st February 2025 |
| Author: | Sudipan Saha |
| Publisher: | Elsevier an imprint of Elsevier Science |
| Format: | Paperback |
| Pagination: | 350 pages |
| Series: | Earth Observation |
| Genres: |
Geographical information systems, geodata and remote sensing Enterprise software Operational research Earth sciences |
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.
Deep Learning for Multi-Sensor Earth Observation features in the following genres: Geographical information systems, geodata and remote sensing, Enterprise software, Operational research, Earth sciences
Deep Learning for Multi-Sensor Earth Observation is available in Paperback
Deep Learning for Multi-Sensor Earth Observation was written by Sudipan Saha and published by Elsevier an imprint of Elsevier Science
Deep Learning for Multi-Sensor Earth Observation has 350 pages
Yes it is part of Earth Observation series