Name: Deep Learning for Synthetic Aperture Radar Remote Sensing
SKU: 65599710
Price: 178.15 USD
Availability: InStock
Author: Michael , Neubiberg, Germany) Schmitt;Ronny , Germany) Hansch

portada Deep Learning for Synthetic Aperture Radar Remote Sensing

Deep Learning for Synthetic Aperture Radar Remote Sensing

Formato

Libro Físico

Editorial

Elsevier - Health Sciences Division

Autor

Michael , Neubiberg, Germany) Schmitt;Ronny , Germany) Hansch

Año

2025

N° páginas

350

Encuadernación

Tapa Blanda

Dimensiones

22.9x15.3x2 cm

ISBN13

9780443363443

Deep Learning for Synthetic Aperture Radar Remote Sensing

Michael , Neubiberg, Germany) Schmitt;Ronny , Germany) Hansch (Autor) · Elsevier - Health Sciences Division · Tapa Blanda

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Reseña del libro "Deep Learning for Synthetic Aperture Radar Remote Sensing"

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.