Deep Learning for Multi-Sensor Earth Observation

Erscheinungsjahr 2025
ISBN: 978-0-443-26484-9
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 152 mm x 229 mm

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.

Autoren/Hrsg.

Saha, Sudipan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Geowissenschaften Geologie GIS, Geoinformatik
• Geowissenschaften Geographie | Raumplanung Geodäsie, Kartographie, GIS, Fernerkundung
• Geowissenschaften Geologie Geologie

Inhaltsverzeichnis

Section 1: Introduction to Multi-Sensor Data and Artificial Intelligence
1. Deep Learning for Multisensor Earth Observation: Introductory Notes
2. A Basic Introduction to Deep Learning

Section 2: Artificial Intelligence for Sensor-specific data analysis and fusion
3. Deep learning processing of remotely sensed multispectral images
4. Deep Learning and Hyperspectral Images
5. Synthetic Aperture Radar Image Analysis in Era of Deep Learning
6. Deep Learning with Lidar for Earth Observation
7. Several Sensors and Modalities

Section 3: Advanced Concepts and Architectures
8. Self-Supervised Learning for Multimodal Earth Observation Data
9. Vision Transformers and Multisensor Earth Observation
10. Graph Neural Networks for Multi-Sensor Earth Observation
11. Uncertainty Quantification in Deep Neural Networks for Multisensor Earth Observation

Section 4: Multi-sensor Deep Learning Applications
12. Multi-Sensor Deep Learning for Change Detection
13. Multi-Sensor Deep Learning for Glacier Mapping
14. Deep Learning in Multisensor Agriculture and Crop Management
15. Miscellaneous Applications of Deep Learning based Multisensor Earth Observation
16. Multi-Sensor Earth Observation: Outlook

Über Autor(innen)

Saha, Sudipan
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.