Advertisement

© 2020

Hyperspectral Image Analysis

Advances in Machine Learning and Signal Processing

  • Saurabh Prasad
  • Jocelyn Chanussot
Book

Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Table of contents

  1. Front Matter
    Pages i-vi
  2. Saurabh Prasad, Jocelyn Chanussot
    Pages 1-4
  3. Álvaro Moreno-Martínez, María Piles, Jordi Muñoz-Marí, Manuel Campos-Taberner, Jose E. Adsuara, Anna Mateo et al.
    Pages 5-35
  4. Sebastian Berisha, Farideh Foroozandeh Shahraki, David Mayerich, Saurabh Prasad
    Pages 37-68
  5. Farideh Foroozandeh Shahraki, Leila Saadatifard, Sebastian Berisha, Mahsa Lotfollahi, David Mayerich, Saurabh Prasad
    Pages 69-115
  6. Zebin Wu, Yang Xu, Jianjun Liu
    Pages 233-257
  7. Wei Zhu, Zuoqiang Shi, Stanley Osher
    Pages 295-317
  8. Fariborz Taherkhani, Jeremy Dawson, Nasser M. Nasrabadi
    Pages 319-350
  9. Amanda Ziemann, Stefania Matteoli
    Pages 351-375
  10. Shaoquan Zhang, Yuanchao Su, Xiang Xu, Jun Li, Chengzhi Deng, Antonio Plaza
    Pages 377-405
  11. Jingxiang Yang, Yong-Qiang Zhao, Jonathan Cheung-Wai Chan
    Pages 407-433
  12. Ahmad W. Bitar, Jean-Philippe Ovarlez, Loong-Fah Cheong, Ali Chehab
    Pages 435-462
  13. Back Matter
    Pages 463-466

About this book

Introduction

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.

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.


Keywords

Hyperspectral Image Analysis Manifold Learning Subspace Learning Computational Imaging Target Recognition Anomaly Detection Sparse Representations Deep Learning Remote Sensing

Editors and affiliations

  • Saurabh Prasad
    • 1
  • Jocelyn Chanussot
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of HoustonHoustonUSA
  2. 2.CNRS, Grenoble INP, GIPSA-labUniversité Grenoble AlpesGrenobleFrance

About the editors

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.

Bibliographic information

Industry Sectors
Automotive
Chemical Manufacturing
Health & Hospitals
Biotechnology
Electronics
IT & Software
Telecommunications
Law
Consumer Packaged Goods
Pharma
Materials & Steel
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences
Engineering