Remote Sensing Big Data Classification with High Performance Distributed Deep Learning
by
Rocco Sedona 1,2,3,4,*,†
, Gabriele Cavallaro 2,3,4,†, Jenia Jitsev 2,4,†, Alexandre Strube 2, Morris Riedel 1,2,3,4 and Jón Atli Benediktsson 1
Rocco Sedona 1,2,3,4,*,†, Gabriele Cavallaro 2,3,4,†, Jenia Jitsev 2,4,†, Alexandre Strube 2, Morris Riedel 1,2,3,4 and Jón Atli Benediktsson 1
1
School of Engineering and Natural Sciences, University of Iceland, Dunhagi 5, 107 Reykjavík, Iceland
2
Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich (FZJ), Wilhelm-Johnen-Strasse 1, 52425 Jülich, Germany
3
High Productivity Data Processing Research Group, JSC, 52425 Jülich, Germany
4
Cross-Sectional Team Deep Learning (CST-DL), JSC, 52425 Jülich, Germany
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2019, 11(24), 3056; https://doi.org/10.3390/rs11243056
Received: 16 October 2019 / Revised: 26 November 2019 / Accepted: 11 December 2019 / Published: 17 December 2019
(This article belongs to the Special Issue Analysis of Big Data in Remote Sensing)
High-Performance Computing (HPC) has recently been attracting more attention in remote sensing applications due to the challenges posed by the increased amount of open data that are produced daily by Earth Observation (EO) programs. The unique parallel computing environments and programming techniques that are integrated in HPC systems are able to solve large-scale problems such as the training of classification algorithms with large amounts of Remote Sensing (RS) data. This paper shows that the training of state-of-the-art deep Convolutional Neural Networks (CNNs) can be efficiently performed in distributed fashion using parallel implementation techniques on HPC machines containing a large number of Graphics Processing Units (GPUs). The experimental results confirm that distributed training can drastically reduce the amount of time needed to perform full training, resulting in near linear scaling without loss of test accuracy.
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Keywords:
distributed deep learning; high performance computing; residual neural network; convolutional neural network; classification; sentinel-2
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Sedona, R.; Cavallaro, G.; Jitsev, J.; Strube, A.; Riedel, M.; Benediktsson, J.A. Remote Sensing Big Data Classification with High Performance Distributed Deep Learning. Remote Sens. 2019, 11, 3056.
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