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. Dieser Artikel ist nicht verfügbar.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

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.

Dr. Tatireddy Subba Reddy, Assistant Professor at B V Raju Institute of Technology, has 6 years of teaching and 3 years of research experience. With a Ph.D. from VIT-AP University, he holds a Master's Degree from JNTU, Kakinada. He authored 20+ research articles, an Indian patent, and the book Deep Learning and Its Applications.