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Distributed Hyperspectral Image Analysis Based on Cloud Computing Architectures.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Distributed Hyperspectral Image Analysis Based on Cloud Computing Architectures./
Author:	Quirita, Victor Andres Ayma.
Description:	1 online resource (76 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-06, Section: A.
Contained By:	Dissertations Abstracts International84-06A.
Subject:	Work stations. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30190106click for full text (PQDT)
ISBN:	9798358429031

Distributed Hyperspectral Image Analysis Based on Cloud Computing Architectures.
Quirita, Victor Andres Ayma.

Distributed Hyperspectral Image Analysis Based on Cloud Computing Architectures. - 1 online resource (76 pages)

Source: Dissertations Abstracts International, Volume: 84-06, Section: A.

Thesis (Ph.D.)--Pontificia Universidad Catolica del Peru (Peru), 2022.

Includes bibliographical references

In this thesis, we introduce a novel distributed version of the N-FINDR endmember extraction algorithm, which is able to exploit computer cluster resources in order to efficiently process large volumes of hyperspectral remote sensing data. The implementation of the distributed algorithm was done by extending the InterCloud Data Mining Package capabilities, originally adopted for land cover classification, through the HyperCloud-RS framework, here adapted for performing endmember extraction processes, which can be likewise executed on cloud computing environments, allowing users to elastically access and exploit processing power and storage space within cloud computing architectures, for adequately processing large volumes of hyperspectral data. The framework supports distributed execution, network communication, and fault tolerance, transparently and efficiently to the user. The experimental analysis addresses the performance issues, assessing both accuracy and execution time, over the processing of different synthetic versions of the AVIRIS Cuprite hyperspectral dataset, with 3.1 Gb, 6.2 Gb, and 15.1Gb respectively, thus addressing the issue of dealing with large-scale hyperspectral data. As a further contribution of this work, we describe in detail how to extend the HyperCloud-RS framework by integrating new endmember extraction algorithms within the proposed architecture, thus enabling researchers to implement their own distributed endmember extraction approaches specifically designed for processing large volumes of hyperspectral data.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798358429031Subjects--Topical Terms:

3681929
Work stations.
Index Terms--Genre/Form:

542853
Electronic books.

Distributed Hyperspectral Image Analysis Based on Cloud Computing Architectures.
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100 1 $a Quirita, Victor Andres Ayma. $3 3694569
245 1 0 $a Distributed Hyperspectral Image Analysis Based on Cloud Computing Architectures.
264 0 $c 2022
300 $a 1 online resource (76 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 84-06, Section: A.
500 $a Advisor: Castanon, Cesar Armando Beltran.
502 $a Thesis (Ph.D.)--Pontificia Universidad Catolica del Peru (Peru), 2022.
504 $a Includes bibliographical references
520 $a In this thesis, we introduce a novel distributed version of the N-FINDR endmember extraction algorithm, which is able to exploit computer cluster resources in order to efficiently process large volumes of hyperspectral remote sensing data. The implementation of the distributed algorithm was done by extending the InterCloud Data Mining Package capabilities, originally adopted for land cover classification, through the HyperCloud-RS framework, here adapted for performing endmember extraction processes, which can be likewise executed on cloud computing environments, allowing users to elastically access and exploit processing power and storage space within cloud computing architectures, for adequately processing large volumes of hyperspectral data. The framework supports distributed execution, network communication, and fault tolerance, transparently and efficiently to the user. The experimental analysis addresses the performance issues, assessing both accuracy and execution time, over the processing of different synthetic versions of the AVIRIS Cuprite hyperspectral dataset, with 3.1 Gb, 6.2 Gb, and 15.1Gb respectively, thus addressing the issue of dealing with large-scale hyperspectral data. As a further contribution of this work, we describe in detail how to extend the HyperCloud-RS framework by integrating new endmember extraction algorithms within the proposed architecture, thus enabling researchers to implement their own distributed endmember extraction approaches specifically designed for processing large volumes of hyperspectral data.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Work stations. $3 3681929
650 4 $a Power. $3 518736
650 4 $a Image retrieval. $3 3562846
650 4 $a Sensors. $3 3549539
650 4 $a Data processing. $3 680224
650 4 $a Remote sensing systems. $3 3685581
650 4 $a Algorithms. $3 536374
650 4 $a Fault tolerance. $3 3561030
650 4 $a Cloud computing. $3 1016782
650 4 $a Satellites. $3 924316
650 4 $a High performance computing. $3 591827
650 4 $a Data compression. $3 3681696
650 4 $a Field programmable gate arrays. $3 666370
650 4 $a Distributed processing. $3 3680534
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Computer engineering. $3 621879
650 4 $a Computer science. $3 523869
650 4 $a Engineering. $3 586835
650 4 $a Remote sensing. $3 535394
655 7 $a Electronic books. $2 lcsh $3 542853
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690 $a 0984
690 $a 0501
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710 2 $a Pontificia Universidad Catolica del Peru (Peru). $3 3689663
773 0 $t Dissertations Abstracts International $g 84-06A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30190106 $z click for full text (PQDT)