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000867894 1001_ $$0P:(DE-Juel1)178695$$aSedona, Rocco$$b0$$eCorresponding author
000867894 245__ $$aRemote Sensing Big Data Classification with High Performance Distributed Deep Learning
000867894 260__ $$aBasel$$bMDPI$$c2019
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000867894 520__ $$aHigh-Performance Computing (HPC) has recently been attracting more attention in remote sensing applications due to the challenges posed by the increased amount of open data that are produced daily by Earth Observation (EO) programs. The unique parallel computing environments and programming techniques that are integrated in HPC systems are able to solve large-scale problems such as the training of classification algorithms with large amounts of Remote Sensing (RS) data. This paper shows that the training of state-of-the-art deep Convolutional Neural Networks (CNNs) can be efficiently performed in distributed fashion using parallel implementation techniques on HPC machines containing a large number of Graphics Processing Units (GPUs). The experimental results confirm that distributed training can drastically reduce the amount of time needed to perform full training, resulting in near linear scaling without loss of test accuracy.
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000867894 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b1
000867894 7001_ $$0P:(DE-Juel1)158080$$aJitsev, Jenia$$b2
000867894 7001_ $$0P:(DE-Juel1)140202$$aStrube, Alexandre$$b3
000867894 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b4$$ufzj
000867894 7001_ $$00000-0003-0621-9647$$aBenediktsson, Jón Atli$$b5
000867894 773__ $$0PERI:(DE-600)2513863-7$$a10.3390/rs11243056$$gVol. 11, no. 24, p. 3056 -$$n24$$p3056 -$$tRemote sensing$$v11$$x2072-4292$$y2019
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