Deep learning for hyperspectral image analysis and classification / Linmi Tao, Atif Mighees.
Tao, Linmi, author.; Mughees, Atif, author.
2021
TA1637
Available Online

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Title Deep learning for hyperspectral image analysis and classification / Linmi Tao, Atif Mighees.
Author Tao, Linmi, author.
ISBN 9789813344204 (electronic book)
9813344202 (electronic book)
9813344199
9789813344198
DOI https://doi.org/10.1007/978-981-33-4420-4
Published Singapore : Springer, [2021]
Language English
Description 1 online resource
Other Standard Identifiers 10.1007/978-981-33-4420-4 doi
Call Number TA1637
Dewey Decimal Classification 006.4/2
Summary This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed March 17, 2021).
Added Author Mughees, Atif, author.
Series Engineering applications of computational methods ; 5.
Available in Other Form Print version: 9789813344198
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Introduction
Hyperspectral image and classification approaches
Unsupervised hyperspectral image noise reduction and band categorization
Hyperspectral image spatial feature extraction via segmentation
Integrating spectral-spatial information for deep learnign based HSI classification
Multi-deep net based hyperspectral image classification
Sparse-based hyperspectral data classification
Challenges and future prospects.

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