Hyperspectral Image Analysis

Advances in Machine Learning and Signal Processing
1. Auflage 2020
ISBN: 978-3-030-38619-1
Verlag: Springer International Publishing

Buch, Englisch, 466 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 709 g

Reihe: Advances in Computer Vision and Pattern Recognition

This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas ofimage analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.

Zielgruppe

Research

Autoren/Hrsg.

Chanussot, Jocelyn

Prasad, Saurabh

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Geowissenschaften Geologie GIS, Geoinformatik

Inhaltsverzeichnis

1. Introduction.- 2. Machine Learning Methods for Spatial and Temporal Parameter Estimation.- 3. Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms.- 4. Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote Sensing and Biomedicine.- 5. Advances in Deep Learning for Hyperspectral Image Analysis - Addressing Challenges Arising in Practical Imaging Scenarios.- 6. Addressing the Inevitable Imprecision: Multiple Instance Learning for Hyperspectral Image Analysis.

Über Autor(innen)

Dr. Saurabh Prasad is an Associate Professor at the Department of Electrical and Computer Engineering at the University of Houston, TX, USA.

Dr. Jocelyn Chanussot is a Professor in the Signal and Images Department at Grenoble Institute of Technology, France.