Journal Article FZJ-2019-06496

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Remote Sensing Big Data Classification with High Performance Distributed Deep Learning

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2019
MDPI Basel

Remote sensing 11(24), 3056 - () [10.3390/rs11243056]

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Abstract: 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.

Classification:

Contributing Institute(s):
  1. Jülich Supercomputing Center (JSC)
Research Program(s):
  1. 512 - Data-Intensive Science and Federated Computing (POF3-512) (POF3-512)
  2. 511 - Computational Science and Mathematical Methods (POF3-511) (POF3-511)
  3. PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) (PHD-NO-GRANT-20170405)

Appears in the scientific report 2019
Database coverage:
Medline ; Creative Commons Attribution CC BY 4.0 ; DOAJ ; OpenAccess ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; Ebsco Academic Search ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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 Record created 2019-12-11, last modified 2022-09-30


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