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

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Zhu Xiao Xiang, Tuia Devis, Mou Lichao, Xia Gui Song, Zhang Liangpei, Xu Feng, Fraundorfer Friedrich,
Project Multimodal machine learning for remote sensing information fusion
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Original article (peer-reviewed)

Journal IEEE Geoscience and Remote Sensing Magazine
Volume (Issue) 5(4)
Page(s) 8 - 36
Title of proceedings IEEE Geoscience and Remote Sensing Magazine
DOI 10.1109/mgrs.2017.2762307

Open Access

URL https://arxiv.org/abs/1710.03959
Type of Open Access Repository (Green Open Access)

Abstract

© 2013 IEEE. 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.
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