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Mastering Hyperspectral Imaging using ML and Spatial-Spectral Features
Subba Reddy Tatireddy
(Author)
·
Sai Chandana B
(Author)
·
Ravi Kumar Balleda
(Author)
·
LAP Lambert Academic Publishing
· Paperback
Mastering Hyperspectral Imaging using ML and Spatial-Spectral Features - Tatireddy, Subba Reddy ; B, Sai Chandana ; Balleda, Ravi Kumar
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Origin: U.S.A.
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Synopsis "Mastering Hyperspectral Imaging using ML and Spatial-Spectral Features"
This book introduces hyperspectral remote sensing as a transformative imaging technology, capturing intricate details across multiple spectral bands. Originating from a doctoral thesis, the book bridges academic exploration and practical applications in hyperspectral image classification. It pioneers novel methodologies using deep learning and machine learning, featuring the Deep Adversarial Learning Framework for enhanced accuracy. The text explores groundbreaking approaches employing principal component analysis, empirical mode decomposition, and Support Vector Machines. A semi-supervised classification method inspired by Cycle-GANs is also presented. The book aims to offer a comprehensive understanding of hyperspectral imaging, its methodologies, and practical implications, serving as a valuable resource for students, researchers, and practitioners in the field.
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All books in our catalog are Original.
The book is written in English.
The binding of this edition is Paperback.
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