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Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
被引:0
|作者:
Zhu X.X.
[1
]
Tuia D.
[2
]
Mou L.
[3
]
Xia G.-S.
[4
]
Zhang L.
[4
]
Xu F.
[5
]
Fraundorfer F.
[6
]
机构:
[1] GeoInformation Science and Remote Sensing Laboratory, Wageningen University
[2] German Aerospace Center (DLR), Technical University of Munich
[3] State Key Laboratory of Information Engineering, Surveying, Mapping, and Remote Sensing, Wuhan University
[4] University of Kentucky, University of North Carolina, Lexington, CH
来源:
|
1600年
/
Institute of Electrical and Electronics Engineers Inc.卷
/
05期
基金:
中国国家自然科学基金;
欧洲研究理事会;
美国国家科学基金会;
欧盟地平线“2020”;
关键词:
197;
D O I:
10.1109/MGRS.2017.2762307
中图分类号:
学科分类号:
摘要:
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. © 2013 IEEE.
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页码:8 / 36
页数:28
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