Deep Learning for Multi-Sensor Earth Observation eBook

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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

This is a digital product.

Deep Learning for Multi-Sensor Earth Observation is written by Sudipan Saha and published by Elsevier (S&T). The Digital and eTextbook ISBNs for Deep Learning for Multi-Sensor Earth Observation are 9780443264856, 0443264856 and the print ISBNs are 9780443264849, 0443264848.