Hyperspectral image analysis : advances in machine learning and signal processing

Responsibility: Saurabh Prasad, Jocelyn Chanussot, editors.
Publication: Cham : Springer, 2020.
Physical description: 1 online resource (464 pages)
Series: Advances in computer vision and pattern recognition.

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Description

Creators/Contributors

Contributor: Prasad, Saurabh, 1980-; Chanussot, Jocelyn.

Contents/Summary

Contents

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.-
7. Supervised, Semi Supervised and Unsupervised Learning for Hyperspectral Regression.-
8. Sparsity-based Methods for Classification.-
9. Multiple Kernel Learning for Hyperspectral Image Classification.-
10. Low Dimensional Manifold Model in Hyperspectral Image Reconstruction.-
11. Deep Sprase Band Selection for Hyperspectral Face Recognition.-
12. Detection of Large-Scale and Anomalous Changes.-
13. Recent Advances in Hyperspectral Unmixing Using Sparse Techniques and Deep Learning.-
14. Chapter Hyperspectral-Multispectral Image Fusion Enhancement Based on Deep Learning.-
15. Automatic Target Detection for Sparse Hyperspectral Images.
(source: Nielsen Book Data)

Publisher's summary

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. .
(source: Nielsen Book Data)

Subjects

Subjects: Hyperspectral imaging.; Image processing.; Artificial intelligence.; Geographical information systems (GIS) & remote sensing.; Imaging systems & technology.; Computers > Intelligence (AI) & Semantics.; Technology & Engineering > Remote Sensing & Geographic Information Systems.; Technology & Engineering > Electronics > General.; Computers > Computer Graphics.

Bibliographic information

Publication date: 2020
Series: Advances in Computer Vision and Pattern Recognition
Note: 6.1 Motivating Examples for Multiple Instance Learning in Hyperspectral Analysis
ISBN: 9783030386177 (electronic book); 3030386171 (electronic book)

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