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A GPU Accelerated Hyperspectral 3D Convolutional Neural Network Classification at the Edge with Principal Component Analysis Preprocessing

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Parallel Processing and Applied Mathematics (PPAM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13827))

Abstract

The Edge Computing paradigm promises to transfer decision-making processes based on artificial intelligence algorithms to the edge of the network without the need to query servers far from the data collection point. Hyperspectral image classification is one of the application fields that can benefit most from the close relationship between Edge Computing and Artificial Intelligence. It consists of a framework of techniques and methodologies for collecting and processing images related to objects or scenes on the Earth’s surface, employing cameras or other sensors mounted on Unmanned Aerial Vehicles. However, the computing performance of the edge devices is not comparable with those of high-end servers, so specific approaches are required to consider the influence of the computing environment on the algorithm development methodology. In the present work, we propose a hybrid technique to make the Hyperspectral Image classification through Convolutional Neural Network affordable on low-power and high-performance sensor devices. We first use the Principal Component Analysis to filter insignificant wavelengths to reduce the dataset dimension; then, we use a process acceleration strategy to improve the performance by introducing a GPU-based form of parallelism.

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Notes

  1. 1.

    Source code: https://github.com/gigernau/PCAHyperspectralClassifier.

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Authors and Affiliations

  1. Institute for High Performance Computing and Networking (ICAR), CNR, 80131, Naples, Italy

    Gianluca De Lucia & Diego Romano

  2. Department of Mathematics and Applications, University of Naples Federico II, 80126, Naples, Italy

    Marco Lapegna

Authors
  1. Gianluca De Lucia
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  2. Marco Lapegna
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  3. Diego Romano
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Corresponding author

Correspondence to Gianluca De Lucia .

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Editors and Affiliations

  1. Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

  2. University of Tennessee, Knoxville, TN, USA

    Jack Dongarra

  3. University of Southern California, Marina del Rey, CA, USA

    Ewa Deelman

  4. Czestochowa University of Technology, Czestochowa, Poland

    Konrad Karczewski

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De Lucia, G., Lapegna, M., Romano, D. (2023). A GPU Accelerated Hyperspectral 3D Convolutional Neural Network Classification at the Edge with Principal Component Analysis Preprocessing. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2022. Lecture Notes in Computer Science, vol 13827. Springer, Cham. https://doi.org/10.1007/978-3-031-30445-3_11

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  • DOI: https://doi.org/10.1007/978-3-031-30445-3_11

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  • Online ISBN: 978-3-031-30445-3

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