articleIEEE Geoscience and Remote Sensing MagazineDec 1, 2017GREEN OA

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

XXXiao Xiang ZhuDTDevis TuiaLMLichao MouGXGui-Song XiaLZLiangpei Zhang

Technical University of Munich · Wageningen University & Research · +4 more institutions

Indexed inarxivcrossref

Abstract

Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their…

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2,947
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Authors

7
  • XX
    Xiao Xiang ZhuCorresponding

    Technical University of Munich

  • DT
    Devis Tuia

    Wageningen University & Research

  • LM
    Lichao Mou

    Technical University of Munich

  • GX
    Gui-Song Xia

    Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

  • LZ
    Liangpei Zhang

    Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Topics & keywords

Topics
Keywords
  • Deep learning
  • Looming
  • Key (lock)
  • Deep time
  • Paradigm shift
  • Deep water
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