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Dimensionality reduction of hyperspectral imagery

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dimensionality reduction of hyperspectral imageryby Arati Paul, Nabendu Chaki.
Author:	Paul, Arati.
other author:	Chaki, Nabendu.
Published:	Cham :Springer International Publishing :2024.
Description:	xviii, 116 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Hyperspectral imaging.
Online resource:	https://doi.org/10.1007/978-3-031-42667-4
ISBN:	9783031426674$q(electronic bk.)

Dimensionality reduction of hyperspectral imagery
Paul, Arati.

Dimensionality reduction of hyperspectral imagery[electronic resource] /by Arati Paul, Nabendu Chaki. - Cham :Springer International Publishing :2024. - xviii, 116 p. :ill. (some col.), digital ;24 cm.

Introduction -- Remote sensing -- Digital image processing -- Hyperspectral image characteristics -- Dimensionality reduction -- Dataset description -- Pooling based band extraction -- Ranking based band selection -- Band optimization -- Data Driven approach -- Conclusion.

This book provides information about different types of dimensionality reduction (DR) methods and their effectiveness in hyperspectral data processing. The authors first explain how hyperspectral imagery (HSI) plays an important role in remote sensing due to its high spectral resolution that enables better identification of different materials on the earth's surface. The authors go on to describe potential challenges due to HSI being acquired in hundreds of narrow and contiguous bands, represented as a 3-dimensional image cube, often causing the bands to contain information redundancy. They then show how processing a large number of bands adds challenges in terms of computation complexity that reduces efficiency. The authors then present how DR is an essential step in hyperspectral data analysis to solve these issues. Overall, the book helps readers understand the DR processes and its impact in effective HSI analysis. Presents a data driven approach for dimensionality reduction (DR); Discusses the effect of spatial dimension and noise in the context of DR of hyperspectral imagery (HSI); Includes an optimization based approach for DR challenges and identification of gap areas in existing algorithms along with suitable solutions.

ISBN: 9783031426674$q(electronic bk.)

Standard No.: 10.1007/978-3-031-42667-4doiSubjects--Topical Terms:

972101
Hyperspectral imaging.

LC Class. No.: TR267.733.H9

Dewey Class. No.: 621.367

Dimensionality reduction of hyperspectral imagery
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245 1 0 $a Dimensionality reduction of hyperspectral imagery $h [electronic resource] / $c by Arati Paul, Nabendu Chaki.
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300 $a xviii, 116 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Introduction -- Remote sensing -- Digital image processing -- Hyperspectral image characteristics -- Dimensionality reduction -- Dataset description -- Pooling based band extraction -- Ranking based band selection -- Band optimization -- Data Driven approach -- Conclusion.
520 $a This book provides information about different types of dimensionality reduction (DR) methods and their effectiveness in hyperspectral data processing. The authors first explain how hyperspectral imagery (HSI) plays an important role in remote sensing due to its high spectral resolution that enables better identification of different materials on the earth's surface. The authors go on to describe potential challenges due to HSI being acquired in hundreds of narrow and contiguous bands, represented as a 3-dimensional image cube, often causing the bands to contain information redundancy. They then show how processing a large number of bands adds challenges in terms of computation complexity that reduces efficiency. The authors then present how DR is an essential step in hyperspectral data analysis to solve these issues. Overall, the book helps readers understand the DR processes and its impact in effective HSI analysis. Presents a data driven approach for dimensionality reduction (DR); Discusses the effect of spatial dimension and noise in the context of DR of hyperspectral imagery (HSI); Includes an optimization based approach for DR challenges and identification of gap areas in existing algorithms along with suitable solutions.
650 0 $a Hyperspectral imaging. $3 972101
650 0 $a Dimension reduction (Statistics) $3 592293
650 0 $a Image processing $x Digital techniques. $3 182119
650 0 $a Remote sensing $x Data processing. $3 226296
650 1 4 $a Signal, Speech and Image Processing. $3 919570
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 274492
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Geographical Information System. $3 913165
700 1 $a Chaki, Nabendu. $3 470402
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