Remote sensing data processing acceleration based on multi-core processors

Zheng, Xiao; Xue, Yong; Jin, Guang; Liu, Jia

doi:10.1109/igarss.2016.7729161

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…It is an effective way to construct HPC systems to improve computational efficiency of hyperspectral data processing, especially when the hyperspectral data processing algorithms and applications are highly heterogeneous. For example, Zheng et al [19] have designed and implemented parallel computing methods of pre-processing the time-consuming interpolation based on OpenMP programming model. These methods can significantly improve CPU efficiency and reduce the runtime of the aerosol optical depth retrieval application.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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“…On the other hand, parallel solutions based on multicore CPUs [10,24,25], graphics processing units (GPUs) [14,[26][27][28][29], and field programmable gate arrays (FPGAs) [30][31][32][33][34] were also successful, leading to near real-time performance in many HSI applications [35]. These platforms generally provide a cheaper solution when compared to large-scale distributed systems.…”

Section: Introductionmentioning

confidence: 99%

A New GPU Implementation of Support Vector Machines for Fast Hyperspectral Image Classification

et al. 2020

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The storage and processing of remotely sensed hyperspectral images (HSIs) is facing important challenges due to the computational requirements involved in the analysis of these images, characterized by continuous and narrow spectral channels. Although HSIs offer many opportunities for accurately modeling and mapping the surface of the Earth in a wide range of applications, they comprise massive data cubes. These huge amounts of data impose important requirements from the storage and processing points of view. The support vector machine (SVM) has been one of the most powerful machine learning classifiers, able to process HSI data without applying previous feature extraction steps, exhibiting a robust behaviour with high dimensional data and obtaining high classification accuracies. Nevertheless, the training and prediction stages of this supervised classifier are very time-consuming, especially for large and complex problems that require an intensive use of memory and computational resources. This paper develops a new, highly efficient implementation of SVMs that exploits the high computational power of graphics processing units (GPUs) to reduce the execution time by massively parallelizing the operations of the algorithm while performing efficient memory management during data-reading and writing instructions. Our experiments, conducted over different HSI benchmarks, demonstrate the efficiency of our GPU implementation.

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State of the Art: High-Performance and High-Throughput Computing for Remote Sensing Big Data

Zhang

Xue

Zhou

et al. 2022

IEEE Geosci. Remote Sens. Mag.

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Remote sensing data processing acceleration based on multi-core processors

Cited by 3 publications

References 10 publications

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

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

A New GPU Implementation of Support Vector Machines for Fast Hyperspectral Image Classification

State of the Art: High-Performance and High-Throughput Computing for Remote Sensing Big Data

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