Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing (1st ed. 2020) (Advances in Computer Vision and Pattern Recognition)

Synopsis

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.

Copyright:

2020

Book Details

Book Quality:

Publisher Quality

ISBN-13:

9783030386177

Related ISBNs:

9783030386160

Publisher:

Springer International Publishing

Date of Addition:

05/02/20

Copyrighted By:

Springer

Adult content:

No

Language:

English

Has Image Descriptions:

No

Categories:

Nonfiction, Art and Architecture, Computers and Internet, Technology

Submitted By:

Bookshare Staff

Usage Restrictions:

This is a copyrighted book.

Edited by:

Saurabh Prasad

Edited by:

Jocelyn Chanussot