Deep Learning for Multi-Sensor Earth Observation - Softcover

Softcover

ISBN 10: 0443264848 ISBN 13: 9780443264849

Publisher: Elsevier, 2025

Synopsis

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.

• Addresses the problem of unwieldy datasets from multi-sensor observations, applying Deep Learning to multi-sensor data integration from disparate sources with different resolution and quality
• Provides a thorough foundational reference to Deep Learning applications for handling Earth Observation multi-sensor data across a variety of geosciences
• Includes case studies and real-world data/examples allowing readers to better grasp how to put Deep Learning techniques and methods into practice

About this title

About the Author

Sudipan Saha is currently an Assistant Professor at Yardi School of Artificial Intelligence, Indian Institute of Technology (IIT) Delhi, New Delhi, India. Previously, he worked as a postdoctoral researcher at the Artificial Intelligence for Earth Observation (AI4EO) Lab, Technical University of Munich, Germany (2020-2022). He received a Ph.D. degree in Information and Communication Technologies from the University of Trento and Fondazione Bruno Kessler (FBK), Trento, Italy in 2020, working with Dr. Francesca Bovolo and Prof. Lorenzo Bruzzone. He is the recipient of FBK Best Student Award 2020. Previously, he obtained the M.Tech. degree in Electrical Engineering from IIT Bombay, Mumbai, India in 2014 where he is recipient of Postgraduate Color. He worked as an Engineer with TSMC Limited, Hsinchu, Taiwan, from 2015 to 2016. His research interests are related to multi-temporal and multi-sensor satellite image analysis, uncertainty quantification, deep learning, and climate change.

From the Back Cover

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.

Key features

• Addresses the problem of complex multi-sensor datasets, applying Deep Learning to multi-sensor data integration from disparate sources with different resolution and characteristics
• Provides a thorough foundational reference to Deep Learning applications for handling Earth observation multi-sensor data across a variety of applications
• Includes case studies and examples allowing readers to better grasp how to put Deep Learning techniques and methods into practice

About the editor
Sudipan Saha is an Assistant Professor at Yardi School of Artificial Intelligence, Indian Institute of Technology (IIT) Delhi, New Delhi, India. Previously, he worked as a postdoctoral researcher at Technical University of Munich, Germany (2020–2022), and as an Engineer with TSMC Limited, Taiwan (2015–2016). He received his PhD degree from University of Trento, Trento, Italy in 2020. He is the recipient of Fondazione Bruno Kessler Best Student Award. His research interests are related to Earth observation, climate change, multi-sensor learning, uncertainty quantification, and learning with limited labels.

Edition details

• Publisher: Elsevier
• Publication date: 2025
• ISBN 10: 0443264848
• ISBN 13: 9780443264849
• Binding: Paperback
• Language: English
• Edition number: 1
• Number of pages: 350
• Editor: Saha Sudipan