An Unmixing-Based Content Retrieval Method for Hyperspectral Imagery Repository on Cloud Computing Platform

Zheng, Peng; Wu, Zebin; Weixuan, Zhang; Li, Min; Yang, Jiandong; Zhang, Yi; Wei, Zhihui

doi:10.1109/igarss.2018.8517591

Cited by 6 publications

(3 citation statements)

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“…It is challenging to deal with the storage and processing of big remote sensing data simultaneously [23]. Fortunately, cloud computing emerges in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…This implementation is built upon cloud computing and has demonstrated very high performance. In our previous work [23], a parallel CBIR system is proposed, but it is lack of more dynamic cloud resource management (e.g., using restricted virtual resources management softwares) and more endmember information extraction algorithms. Nevertheless, to the best of our knowledge, there are very few efforts in the literature on constructing unified and general-purpose cloud computing architecture for hyperspectral remote imagery.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we propose a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information as a extension of [23]. To accelerate spectral information extraction, we implement a distributed and parallel unmixing method that operates on cloud computing platforms for accelerating the unmixing processing flow.…”

Section: Introductionmentioning

confidence: 99%

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

Zheng

Sun

et al. 2021

Remote Sensing

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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.

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“…It is challenging to deal with the storage and processing of big remote sensing data simultaneously [23]. Fortunately, cloud computing emerges in recent years.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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