Overview

Saha-Deep Learning for Multi-Sensor

Full Product Details

Author:   Sudipan Saha (Assistant Professor, Yardi School of Artificial Intelligence, Indian Institute of Technology (IIT) Delhi, New Delhi, India)
Publisher:   Elsevier - Health Sciences Division
Imprint:   Elsevier - Health Sciences Division
Weight:   1.000kg
ISBN:  

9780443264849

ISBN 10:   0443264848
Pages:   452
Publication Date:   05 February 2025
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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

Reviews

Author Information

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.

Tab Content 6

Author Website:  

Countries Available

All regions