A Distributed N-FINDR Cloud Computing-Based Solution for Endmembers Extraction on Large-Scale Hyperspectral Remote Sensing Data
In this work, 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 data. The implementation of the distributed algorithm was done by extending the In...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/9/2153 |
| _version_ | 1797503073001668608 |
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| author | Victor Andres Ayma Quirita Gilson Alexandre Ostwald Pedro da Costa César Beltrán |
| author_facet | Victor Andres Ayma Quirita Gilson Alexandre Ostwald Pedro da Costa César Beltrán |
| author_sort | Victor Andres Ayma Quirita |
| collection | DOAJ |
| description | In this work, 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 data. The implementation of the distributed algorithm was done by extending the InterCloud Data Mining Package, originally adopted for land cover classification, through the HyperCloud-RS framework, here adapted for endmember extraction, which can be executed on cloud computing environments, allowing users to elastically administer processing power and storage space for adequately handling very large datasets. The framework supports distributed execution, network communication, and fault tolerance, transparently and efficiently to the user. The experimental analysis addresses the performance issues, evaluating 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 other endmember extraction algorithms, thus enabling researchers to implement algorithms specifically designed for their own assessment. |
| first_indexed | 2024-03-10T03:45:17Z |
| format | Article |
| id | doaj.art-c83179233bf04adeaad2605b6fece98e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:45:17Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c83179233bf04adeaad2605b6fece98e2023-11-23T09:11:23ZengMDPI AGRemote Sensing2072-42922022-04-01149215310.3390/rs14092153A Distributed N-FINDR Cloud Computing-Based Solution for Endmembers Extraction on Large-Scale Hyperspectral Remote Sensing DataVictor Andres Ayma Quirita0Gilson Alexandre Ostwald Pedro da Costa1César Beltrán2Department of Engineering, Pontifical Catholic University of Peru, 1801 Universitaria Avenue, San Miguel, Lima 15088, PeruDepartment of Informatics and Computer Science, Rio de Janeiro State University, Rio de Janeiro 20550-900, Rio de Janeiro, BrazilDepartment of Engineering, Pontifical Catholic University of Peru, 1801 Universitaria Avenue, San Miguel, Lima 15088, PeruIn this work, 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 data. The implementation of the distributed algorithm was done by extending the InterCloud Data Mining Package, originally adopted for land cover classification, through the HyperCloud-RS framework, here adapted for endmember extraction, which can be executed on cloud computing environments, allowing users to elastically administer processing power and storage space for adequately handling very large datasets. The framework supports distributed execution, network communication, and fault tolerance, transparently and efficiently to the user. The experimental analysis addresses the performance issues, evaluating 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 other endmember extraction algorithms, thus enabling researchers to implement algorithms specifically designed for their own assessment.https://www.mdpi.com/2072-4292/14/9/2153cloud computinghyperspectral image processingendmember extractionunmixingremote sensinglarge-scale hyperspectral data |
| spellingShingle | Victor Andres Ayma Quirita Gilson Alexandre Ostwald Pedro da Costa César Beltrán A Distributed N-FINDR Cloud Computing-Based Solution for Endmembers Extraction on Large-Scale Hyperspectral Remote Sensing Data Remote Sensing cloud computing hyperspectral image processing endmember extraction unmixing remote sensing large-scale hyperspectral data |
| title | A Distributed N-FINDR Cloud Computing-Based Solution for Endmembers Extraction on Large-Scale Hyperspectral Remote Sensing Data |
| title_full | A Distributed N-FINDR Cloud Computing-Based Solution for Endmembers Extraction on Large-Scale Hyperspectral Remote Sensing Data |
| title_fullStr | A Distributed N-FINDR Cloud Computing-Based Solution for Endmembers Extraction on Large-Scale Hyperspectral Remote Sensing Data |
| title_full_unstemmed | A Distributed N-FINDR Cloud Computing-Based Solution for Endmembers Extraction on Large-Scale Hyperspectral Remote Sensing Data |
| title_short | A Distributed N-FINDR Cloud Computing-Based Solution for Endmembers Extraction on Large-Scale Hyperspectral Remote Sensing Data |
| title_sort | distributed n findr cloud computing based solution for endmembers extraction on large scale hyperspectral remote sensing data |
| topic | cloud computing hyperspectral image processing endmember extraction unmixing remote sensing large-scale hyperspectral data |
| url | https://www.mdpi.com/2072-4292/14/9/2153 |
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