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.
1144821529
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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Mastering Hyperspectral Imaging using ML and Spatial-Spectral Features

Mastering Hyperspectral Imaging using ML and Spatial-Spectral Features

Mastering Hyperspectral Imaging using ML and Spatial-Spectral Features

Mastering Hyperspectral Imaging using ML and Spatial-Spectral Features

Overview

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.

Product Details

ISBN-13: 9786207459094
Publisher: LAP Lambert Academic Publishing
Publication date: 01/12/2024
Pages: 112
Product dimensions: 6.00(w) x 9.00(h) x 0.27(d)
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