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Distributed Hyperspectral Image Analysis Based on Cloud Computing Architectures.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Distributed Hyperspectral Image Analysis Based on Cloud Computing Architectures./
作者:	Quirita, Victor Andres Ayma.
面頁冊數:	1 online resource (76 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-06, Section: A.
Contained By:	Dissertations Abstracts International84-06A.
標題:	Work stations. -
電子資源:	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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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
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650 4 $a Field programmable gate arrays. $3 666370
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650 4 $a Aerospace engineering. $3 1002622
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650 4 $a Remote sensing. $3 535394
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