articleJournal of Applied Remote SensingSep 23, 2017BRONZE OA

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

JEJohn E. BallDTDerek T. AndersonCSChee Seng Chan

Mississippi State University · University of Malaya

Indexed inarxivcrossref

Abstract

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be…

Citation impact

589
total citations
FWCI
40.51
Percentile
100%
References
395
Citations per year

Authors

3
  • JE
    John E. BallCorresponding

    Mississippi State University

  • DT
    Derek T. Anderson

    Mississippi State University

  • CS
    Chee Seng Chan

    University of Malaya

Topics & keywords

Keywords
  • Deep learning
  • Focus (optics)
  • Field (mathematics)
  • Big data
  • Transfer of learning
  • Deep neural networks
  • Artificial neural network
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