TY - JOUR
T1 - Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
AU - Zhu, Xiao Xiang
AU - Tuia, Devis
AU - Mou, Lichao
AU - Xia, Gui-Song
AU - Zhang, Liangpei
AU - Xu, Feng
AU - Fraundorfer, Friedrich
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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.
AB - 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.
U2 - 10.1109/MGRS.2017.2762307
DO - 10.1109/MGRS.2017.2762307
M3 - Article
SN - 2168-6831
VL - 5
SP - 8
EP - 36
JO - IEEE Geoscience and Remote Sensing Magazine
JF - IEEE Geoscience and Remote Sensing Magazine
IS - 4
ER -