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

Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui Song Xia, Liangpei Zhang, Feng Xu, Friedrich Fraundorfer

Research output: Contribution to specialist publicationArticleResearchpeer-review

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 expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization.

Original languageEnglish
Pages8-36
Number of pages29
Volume5
No.4
Specialist publicationIEEE Geoscience and Remote Sensing Magazine
DOIs
Publication statusPublished - 1 Dec 2017

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climate change
Climate change
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Learning systems
urbanization
Deep learning
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ASJC Scopus subject areas

  • Computer Science(all)
  • Instrumentation
  • Earth and Planetary Sciences(all)
  • Electrical and Electronic Engineering

Cite this

Deep Learning in Remote Sensing : A Comprehensive Review and List of Resources. / Zhu, Xiao Xiang; Tuia, Devis; Mou, Lichao; Xia, Gui Song; Zhang, Liangpei; Xu, Feng; Fraundorfer, Friedrich.

In: IEEE Geoscience and Remote Sensing Magazine, Vol. 5, No. 4, 01.12.2017, p. 8-36.

Research output: Contribution to specialist publicationArticleResearchpeer-review

Zhu, Xiao Xiang ; Tuia, Devis ; Mou, Lichao ; Xia, Gui Song ; Zhang, Liangpei ; Xu, Feng ; Fraundorfer, Friedrich. / Deep Learning in Remote Sensing : A Comprehensive Review and List of Resources. In: IEEE Geoscience and Remote Sensing Magazine. 2017 ; Vol. 5, No. 4. pp. 8-36.
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