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Advances in Computer Vision and Pattern Recognition

Hyperspectral Image Analysis

Advances in Machine Learning and Signal Processing

Editors: Prasad, Saurabh, Chanussot, Jocelyn (Eds.)

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  • 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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eBook 96,29 €
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  • ISBN 978-3-030-38617-7
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Hardcover 119,99 €
price for India (gross)
  • ISBN 978-3-030-38616-0
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About this book

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

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)

Table of contents (15 chapters)
  • Introduction

    Pages 1-4

    Prasad, Saurabh (et al.)

  • Machine Learning Methods for Spatial and Temporal Parameter Estimation

    Pages 5-35

    Moreno-Martínez, Álvaro (et al.)

  • Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms

    Pages 37-68

    Berisha, Sebastian (et al.)

  • Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote Sensing and Biomedicine

    Pages 69-115

    Shahraki, Farideh Foroozandeh (et al.)

  • Advances in Deep Learning for Hyperspectral Image Analysis—Addressing Challenges Arising in Practical Imaging Scenarios

    Pages 117-140

    Zhou, Xiong (et al.)

Buy this book

eBook 96,29 €
price for India (gross)
  • ISBN 978-3-030-38617-7
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 119,99 €
price for India (gross)
  • ISBN 978-3-030-38616-0
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
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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

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