Practice and Experience in Using Parallel and Scalable Machine Learning in Remote Sensing from HPC Over Cloud to Quantum Computing

Riedel, Morris; Cavallaro, Gabriele; Benediktsson, Jon Atli

doi:10.1109/IGARSS47720.2021.9554656

Contribution to a conference proceedings/Contribution to a book

FZJ-2021-02865

Practice and Experience in Using Parallel and Scalable Machine Learning in Remote Sensing from HPC Over Cloud to Quantum Computing

Riedel, M. (Corresponding author)FZJ* ; Cavallaro, G.FZJ* ; Benediktsson, J. A.

2021
IEEE

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS : [Proceedings] - IEEE, 2021. - ISBN 978-1-6654-0369-6 - doi:10.1109/IGARSS47720.2021.9554656
IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, Brussels, Belgium, 12 Jul 2021 - 16 Jul 2021 IEEE 1571-1582 (2021) [10.1109/IGARSS47720.2021.9554656]

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Please use a persistent id in citations: http://hdl.handle.net/2128/31337 doi:10.1109/IGARSS47720.2021.9554656

Abstract: Using computationally efficient techniques for transforming the massive amount of Remote Sensing (RS) data into scientific understanding is critical for Earth science. The utilization of efficient techniques through innovative computing systems in RS applications has become more widespread in recent years. The continuously increased use of Deep Learning (DL) as a specific type of Machine Learning (ML) for data-intensive problems (i.e., ’big data’) requires powerful computing resources with equally increasing performance. This paper reviews recent advances in High-Performance Computing (HPC), Cloud Computing (CC), and Quantum Computing (QC) applied to RS problems. It thus represents a snapshot of the state-of-the-art in ML in the context of the most recent developments in those computing areas, including our lessons learned over the last years. Our paper also includes some recent challenges and good experiences by using Europe's fastest supercomputer for hyper-spectral and multi-spectral image analysis with state-of-the-art data analysis tools. It offers a thoughtful perspective of the potential and emerging challenges of applying innovative computing paradigms to RS problems.

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