東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Dimensionality reduction of hyperspectral imagery

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dimensionality reduction of hyperspectral imagery/ by 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.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Hyperspectral imaging. -
Online resource:	https://doi.org/10.1007/978-3-031-42667-4
ISBN:	9783031426674

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

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

3711381
Hyperspectral imaging.

LC Class. No.: TR267.733.H9

Dewey Class. No.: 621.367

Dimensionality reduction of hyperspectral imagery
LDR:02562nmm a2200349 a 4500 001 2367886
003 DE-He213
005 20231004093505.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031426674 $q (electronic bk.)
020 $a 9783031426667 $q (paper)
024 7 $a 10.1007/978-3-031-42667-4 $2 doi
035 $a 978-3-031-42667-4
040 $a GP $c GP
041 0 $a eng
050 4 $a TR267.733.H9
072 7 $a TJF $2 bicssc
072 7 $a UYS $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a TJF $2 thema
072 7 $a UYS $2 thema
082 0 4 $a 621.367 $2 23
090 $a TR267.733.H9 $b P324 2024
100 1 $a Paul, Arati. $3 3711380
245 1 0 $a Dimensionality reduction of hyperspectral imagery $h [electronic resource] / $c by Arati Paul, Nabendu Chaki.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
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 3711381
650 0 $a Dimension reduction (Statistics) $3 1621970
650 0 $a Image processing $x Digital techniques. $3 532550
650 0 $a Remote sensing $x Data processing. $3 567259
650 1 4 $a Signal, Speech and Image Processing. $3 3592727
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 890871
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Geographical Information System. $3 3538564
700 1 $a Chaki, Nabendu. $3 1073885
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-42667-4
950 $a Engineering (SpringerNature-11647)