TY  - JOUR
AU  - Sedona, Rocco
AU  - Cavallaro, Gabriele
AU  - Jitsev, Jenia
AU  - Strube, Alexandre
AU  - Riedel, Morris
AU  - Benediktsson, Jón Atli
TI  - Remote Sensing Big Data Classification with High Performance Distributed Deep Learning
JO  - Remote sensing
VL  - 11
IS  - 24
SN  - 2072-4292
CY  - Basel
PB  - MDPI
M1  - FZJ-2019-06496
SP  - 3056 -
PY  - 2019
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000507333400170
DO  - DOI:10.3390/rs11243056
UR  - https://juser.fz-juelich.de/record/867894
ER  -