Computational Intelligence based Hyperspectral Image Processing - Volume 1

Hyperspectral imaging has revolutionized fields ranging from environmental monitoring to medical diagnostics by providing detailed spectral information across vast datasets. However, the complexity and high dimensionality of hyperspectral data pose significant challenges in analysis and interpretation. "Computational Intelligence based Hyperspectral Image Processing" is a comprehensive guide to overcoming these challenges by leveraging the latest computational intelligence techniques. This Volume comprises of 9 chapters that illustrate how computational intelligence-based solutions has been successful to improve efficiency and productivity, quality control, and in enhancing innovation and competitiveness with cost reduction. This book is an essential resource for researchers, professionals, and students working in remote sensing, image processing, and computational intelligence. Whether you're new to the field or looking to deepen your expertise, this book provides the tools and knowledge you need to succeed in the fast-evolving world of hyperspectral imaging.

List of contents

Unveiling Hyperspectral and Multispectral Image Processing: A Contemporary Exploration of Band Selection, Dimensionality Reduction, and Compressive Sensing Techniques.- Hyperspectral Image Registration: State-of-the-Art and Future Challenges.- Hardware Accelerators for Hyperspectral Image Classification in Space: A Survey on Deep Learning based Architectures for Hardware Accelerators.

Product details

• Assisted by: Abraham (Editor), Ajith Abraham (Editor), Anu Bajaj (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Released: 29.03.2025
• EAN: 9783031831225
• ISBN: 978-3-0-3183122-5
• No. of pages: 218
• Illustrations: X, 218 p. 124 illus., 93 illus. in color.
• Series: Intelligent Systems Reference Library
• Subjects: Natural sciences, medicine, IT, technology > Technology > General, dictionaries; Elektronik, machine learning, Maschinelles Lernen, Datenbanken, Artificial Intelligence, Deep Learning, Maschinelles Sehen, Bildverstehen, Computer Vision, Image Analysis, Digitale Signalverarbeitung (DSP), Data Engineering, Computational Intelligence, Remote sensing, Digital and Analog Signal Processing, Image processing, hyperspectral imaging, Nature-Inspired Algorithms