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Open AccessArticle

A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

1
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2
China Satellite Maritime Tracking and Control Department, Jiangyin 214431, China
3
Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, University of Extremadura, 10071 Cáceres, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 176; https://doi.org/10.3390/rs13020176
Received: 18 November 2020 / Revised: 27 December 2020 / Accepted: 4 January 2021 / Published: 6 January 2021
As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy. View Full-Text
Keywords: content-based image retrieval; cloud computing; unmixing; hyperspectral images content-based image retrieval; cloud computing; unmixing; hyperspectral images
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MDPI and ACS Style

Zheng, P.; Wu, Z.; Sun, J.; Zhang, Y.; Zhu, Y.; Shen, Y.; Yang, J.; Wei, Z.; Plaza, A. A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms. Remote Sens. 2021, 13, 176.

AMA Style

Zheng P, Wu Z, Sun J, Zhang Y, Zhu Y, Shen Y, Yang J, Wei Z, Plaza A. A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms. Remote Sensing. 2021; 13(2):176.

Chicago/Turabian Style

Zheng, Peng; Wu, Zebin; Sun, Jin; Zhang, Yi; Zhu, Yaoqin; Shen, Yuan; Yang, Jiandong; Wei, Zhihui; Plaza, Antonio. 2021. "A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms" Remote Sens. 13, no. 2: 176.

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