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.
| ISBN: | 9783030386191 |
| Publication date: | 29th April 2021 |
| Author: | Saurabh Prasad |
| Publisher: | Springer Nature Switzerland AG |
| Format: | Paperback |
| Pagination: | 466 pages |
| Series: | Advances in Computer Vision and Pattern Recognition |
| Genres: |
Image processing Machine learning Digital signal processing (DSP) Computer vision Geographical information systems, geodata and remote sensing Imaging systems and technology |
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.
Hyperspectral Image Analysis features in the following genres: Image processing, Machine learning, Digital signal processing (DSP), Computer vision, Geographical information systems, geodata and remote sensing, Imaging systems and technology
Paperback, Hardback. £116.99, down from the £129.99 cover price. Not Available.
Hyperspectral Image Analysis was written by Saurabh Prasad and published by Springer Nature Switzerland AG
Hyperspectral Image Analysis has 466 pages
Yes it is part of Advances in Computer Vision and Pattern Recognition series
£116.99, reduced from £129.99. Not Available.