Hyperspectral Image Analysis

Advances in Machine Learning and Signal Processing

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Éditeur :

Springer


Collection :

Advances in Computer Vision and Pattern Recognition

Paru le : 2020-04-27



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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 ofimage 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. 

Pages
466 pages
Collection
Advances in Computer Vision and Pattern Recognition
Parution
2020-04-27
Marque
Springer
EAN papier
9783030386160
EAN PDF
9783030386177

Informations sur l'ebook
Nombre pages copiables
4
Nombre pages imprimables
46
Taille du fichier
22917 Ko
Prix
147,69 €
EAN EPUB
9783030386177

Informations sur l'ebook
Nombre pages copiables
4
Nombre pages imprimables
46
Taille du fichier
100608 Ko
Prix
147,69 €