Mastering Hyperspectral Imaging using ML and Spatial-Spectral Features : An In-Depth Guide to Advanced Techniques and Best Practices in Hyperspectral Image Analysis for Unleashing Insights

Subba Reddy Tatireddy

ISBN 10: 6207459091 / ISBN 13: 9786207459094

Published by LAP LAMBERT Academic Publishing, 2024

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9786207459094

Bibliographic Details

• Title: Mastering Hyperspectral Imaging using ML and Spatial-Spectral Features : An In-Depth Guide to Advanced Techniques and Best Practices in Hyperspectral Image Analysis for Unleashing Insights
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2024
• Language: English
• ISBN 10: 6207459091
• ISBN 13: 9786207459094
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 185 grams

About this title

Synopsis

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.