Gast ::
| Hauptseite > Publikationsdatenbank > Super-Resolution of Large Volumes of Sentinel-2 Images with High Performance Distributed Deep Learning > print |
| Hauptseite > Publikationsdatenbank > Super-Resolution of Large Volumes of Sentinel-2 Images with High Performance Distributed Deep Learning > print |
| 001 | 890969 | ||
| 005 | 20230111074306.0 | ||
| 024 | 7 | _ | |a 10.1109/IGARSS39084.2020.9323734 |2 doi |
| 024 | 7 | _ | |a 0022-7722 |2 ISSN |
| 024 | 7 | _ | |a 1447-073X |2 ISSN |
| 024 | 7 | _ | |a 1447-6959 |2 ISSN |
| 024 | 7 | _ | |a 2128/27350 |2 Handle |
| 024 | 7 | _ | |a WOS:000664335300139 |2 WOS |
| 037 | _ | _ | |a FZJ-2021-01285 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Zhang, Run |0 P:(DE-Juel1)178843 |b 0 |
| 111 | 2 | _ | |a 2020 IEEE International Geoscience and Remote Sensing Symposium |g IGARSS 2020 |c Online event |d 2020-09-26 - 2020-10-02 |w Hawaii |
| 245 | _ | _ | |a Super-Resolution of Large Volumes of Sentinel-2 Images with High Performance Distributed Deep Learning |
| 260 | _ | _ | |c 2020 |
| 300 | _ | _ | |a 617 - 620 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1615460754_5383 |2 PUB:(DE-HGF) |
| 520 | _ | _ | |a This 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. |
| 536 | _ | _ | |a 512 - Data-Intensive Science and Federated Computing (POF3-512) |0 G:(DE-HGF)POF3-512 |c POF3-512 |f POF III |x 0 |
| 536 | _ | _ | |a HBP - The Human Brain Project (604102) |0 G:(EU-Grant)604102 |c 604102 |f FP7-ICT-2013-FET-F |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef Conference |
| 700 | 1 | _ | |a Cavallaro, Gabriele |0 P:(DE-Juel1)171343 |b 1 |e Corresponding author |u fzj |
| 700 | 1 | _ | |a Jitsev, Jenia |0 P:(DE-Juel1)158080 |b 2 |u fzj |
| 773 | _ | _ | |a 10.1109/IGARSS39084.2020.9323734 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/890969/files/Zhang_Run_IGARSS_2020.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:890969 |p openaire |p open_access |p driver |p VDB |p ec_fundedresources |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)178843 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)171343 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)158080 |
| 913 | 0 | _ | |a DE-HGF |b Key Technologies |l Supercomputing & Big Data |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-512 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-500 |4 G:(DE-HGF)POF |v Data-Intensive Science and Federated Computing |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 914 | 1 | _ | |y 2021 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1030 |2 StatID |b Current Contents - Life Sciences |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1040 |2 StatID |b Zoological Record |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b ANAT SCI INT : 2015 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |
| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0310 |2 StatID |b NCBI Molecular Biology Database |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Thomson Reuters Master Journal List |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a contrib |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|