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000890969 0247_ $$2doi$$a10.1109/IGARSS39084.2020.9323734
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000890969 1001_ $$0P:(DE-Juel1)178843$$aZhang, Run$$b0
000890969 1112_ $$a2020 IEEE International Geoscience and Remote Sensing Symposium$$cOnline event$$d2020-09-26 - 2020-10-02$$gIGARSS 2020$$wHawaii
000890969 245__ $$aSuper-Resolution of Large Volumes of Sentinel-2 Images with High Performance Distributed Deep Learning
000890969 260__ $$c2020
000890969 300__ $$a617 - 620
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000890969 520__ $$aThis work proposes a novel distributed deep learning modelfor Remote Sensing (RS) images super-resolution. High PerformanceComputing (HPC) systems with GPUs are used toaccelerate the learning of the unknown low to high resolutionmapping from large volumes of Sentinel-2 data. The proposeddeep learning model is based on self-attention mechanismand residual learning. The results demonstrate that stateof-the-art performance can be achieved by keeping the size ofthe model relatively small. Synchronous data parallelism isapplied to scale up the training process without severe performanceloss. Distributed training is thus shown to speed uplearning substantially while keeping performance intact.
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000890969 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b1$$eCorresponding author$$ufzj
000890969 7001_ $$0P:(DE-Juel1)158080$$aJitsev, Jenia$$b2$$ufzj
000890969 773__ $$a10.1109/IGARSS39084.2020.9323734
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