Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing (Advances in Computer Vision and Pattern Recognition) - Image 1

£107.43

Springer Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing (Advances in Computer Vision and Pattern Recognition)

Name: Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing (Advances in Computer Vision and Pattern Recognition)
Brand: Springer
Price: 107.43 GBP
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AI & Machine Learning

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Description

From the Back Cover 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. Product Description 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 Author 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.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 3030386198
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 29 April 2021
Listed Since: 01 April 2021

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Springer Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing (Advances in Computer Vision and Pattern Recognition)

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