Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing
- 9h 2m
- Jocelyn Chanussot, Saurabh Prasad
- Springer
- 2020
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.
In this Book
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Introduction
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Machine Learning Methods for Spatial and Temporal Parameter Estimation
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Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms
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Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote Sensing and Biomedicine
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Advances in Deep Learning for Hyperspectral Image Analysis—Addressing Challenges Arising in Practical Imaging Scenarios
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Addressing the Inevitable Imprecision: Multiple Instance Learning for Hyperspectral Image Analysis
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Supervised, Semi-Supervised, and Unsupervised Learning for Hyperspectral Regression
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Sparsity-Based Methods for Classification
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Multiple Kernel Learning for Hyperspectral Image Classification
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Low Dimensional Manifold Model in Hyperspectral Image Reconstruction
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Deep Sparse Band Selection for Hyperspectral Face Recognition
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Detection of Large-Scale and Anomalous Changes
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Recent Advances in Hyperspectral Unmixing Using Sparse Techniques and Deep Learning
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Hyperspectral-Multispectral Image Fusion Enhancement Based on Deep Learning
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Automatic Target Detection for Sparse Hyperspectral Images
