Hyperspectral Image Analysis
Advances in Machine Learning and Signal Processing
Editors: Prasad, Saurabh, Chanussot, Jocelyn (Eds.)
Free Preview- Provides a comprehensive review of the state of the art in hyperspectral image analysis
- Presents perspectives from experts who are pioneers in a broad range of signal processing and machine learning fields related to hyperspectral imaging and remote sensing
- Is suitable both as a reference book and as a textbook for advanced graduate courses on multi-dimensional image processing
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- About this book
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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 of image 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.
- About the authors
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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.
- Table of contents (15 chapters)
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Introduction
Pages 1-4
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Machine Learning Methods for Spatial and Temporal Parameter Estimation
Pages 5-35
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Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms
Pages 37-68
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Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote Sensing and Biomedicine
Pages 69-115
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Advances in Deep Learning for Hyperspectral Image Analysis—Addressing Challenges Arising in Practical Imaging Scenarios
Pages 117-140
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Table of contents (15 chapters)
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Bibliographic Information
- Bibliographic Information
-
- Book Title
- Hyperspectral Image Analysis
- Book Subtitle
- Advances in Machine Learning and Signal Processing
- Editors
-
- Saurabh Prasad
- Jocelyn Chanussot
- Series Title
- Advances in Computer Vision and Pattern Recognition
- Copyright
- 2020
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer Nature Switzerland AG
- eBook ISBN
- 978-3-030-38617-7
- DOI
- 10.1007/978-3-030-38617-7
- Hardcover ISBN
- 978-3-030-38616-0
- Series ISSN
- 2191-6586
- Edition Number
- 1
- Number of Pages
- VI, 466
- Number of Illustrations
- 26 b/w illustrations, 144 illustrations in colour
- Topics
