Remote Sensing Big Data Classification with High Performance Distributed Deep Learning

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 24; p. 3056
• Main Authors: Sedona, Rocco, Cavallaro, Gabriele, Jitsev, Jenia, Strube, Alexandre, Riedel, Morris, Benediktsson, Jón
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 17.12.2019
• Subjects: Accuracy; Algorithms; Annotations; Artificial neural networks; Big Data; Classification; Datasets; Deep learning; Graphics processing units; Machine learning; Neural networks; R&D; Remote sensing; Research & development; Satellites; Training
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11243056

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.