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Deep learning for hyperspectral imag...

Tao, Linmi.

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Deep learning for hyperspectral image analysis and classification

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning for hyperspectral image analysis and classification/ by Linmi Tao, Atif Mughees.
Author:	Tao, Linmi.
other author:	Mughees, Atif.
Published:	Singapore :Springer Singapore : : 2021.,
Description:	xii, 207 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Hyperspectral Imaging System -- Classification Techniques for HSI -- Preprocessing: Noise Reduction/ Band Categorization for HSI -- Spatial Feature Extraction Using Segmentation -- Multiple Deep learning models for feature extraction in classification -- Deep learning for merging spatial and spectral information in classification -- Sparse cording for Hyperspectral Data -- Classification Applications of HSI classification -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Hyperspectral imaging - Data processing. -
Online resource:	https://doi.org/10.1007/978-981-33-4420-4
ISBN:	9789813344204

Deep learning for hyperspectral image analysis and classification
Tao, Linmi.

Deep learning for hyperspectral image analysis and classification[electronic resource] /by Linmi Tao, Atif Mughees. - Singapore :Springer Singapore :2021. - xii, 207 p. :ill., digital ;24 cm. - Engineering applications of computational methods,v.52662-3366 ;. - Engineering applications of computational methods ;v.5..

Introduction -- Hyperspectral Imaging System -- Classification Techniques for HSI -- Preprocessing: Noise Reduction/ Band Categorization for HSI -- Spatial Feature Extraction Using Segmentation -- Multiple Deep learning models for feature extraction in classification -- Deep learning for merging spatial and spectral information in classification -- Sparse cording for Hyperspectral Data -- Classification Applications of HSI classification -- Conclusion.

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.

ISBN: 9789813344204

Standard No.: 10.1007/978-981-33-4420-4doiSubjects--Topical Terms:

3490711
Hyperspectral imaging
--Data processing.

LC Class. No.: TA1637 / .T36 2021

Dewey Class. No.: 006.42

Deep learning for hyperspectral image analysis and classification
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300 $a xii, 207 p. : $b ill., digital ; $c 24 cm.
490 1 $a Engineering applications of computational methods, $x 2662-3366 ; $v v.5
505 0 $a Introduction -- Hyperspectral Imaging System -- Classification Techniques for HSI -- Preprocessing: Noise Reduction/ Band Categorization for HSI -- Spatial Feature Extraction Using Segmentation -- Multiple Deep learning models for feature extraction in classification -- Deep learning for merging spatial and spectral information in classification -- Sparse cording for Hyperspectral Data -- Classification Applications of HSI classification -- Conclusion.
520 $a 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.
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650 2 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Signal, Image and Speech Processing. $3 891073
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