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.

Author: Subba Reddy Tatireddy,Sai Chandana B,Ravi Kumar Balleda
Publisher: LAP Lambert Academic Publishing
Published: 01/12/2024
Pages: 112
Binding Type: Paperback
Weight: 0.39lbs
Size: 9.00h x 6.00w x 0.27d
ISBN: 9786207459094

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