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Full Description
Deep Learning for Synthetic Aperture Radar Remote Sensing delves into the transformative synergy between synthetic aperture radar (SAR) and cutting-edge machine learning techniques. Traditionally rooted in signal processing, SAR's active imaging capabilities rise above optical limitations, offering resilience to environmental factors like cloud cover. This book showcases how machine learning augments every stage of SAR image processing, from raw data refinement to advanced information extraction. Through comprehensive coverage of acquisition modes and processing methodologies, including polarimetry and interferometry, this book equips readers with the tools to harness SAR's full potential. Aiming to further enhance remote sensing imaging, it serves as a vital resource for those seeking to integrate SAR data seamlessly into the modern machine learning landscape. Deep Learning for Synthetic Aperture Radar Remote Sensing addresses a critical gap in the intersection of SAR technology and machine learning, offering a pioneering roadmap for researchers and practitioners alike. With its emphasis on modern techniques, it serves as a catalyst for unlocking SAR's untapped potential and shaping the future of Earth observation.
Contents
1. Remote Sensing with Synthetic Aperture Radar (SAR)
2. Machine Learning Basics
3. SAR Image Formation
4. Data Compression
5. Despeckling
6. SAR Interferometry (Phase and Coherence Estimation, Phase Unwrapping)
7. SAR Tomography
8. Single-Image Height Estimation
9. Object Detection
10. Land Cover Classification
11. Change Detection
12. Retrieval of Bio-/geophysical Parameters
13. Future Outlook
