Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate 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.
@article{arxiv.1710.03959,
title = {Deep learning in remote sensing: a review},
author = {Xiao Xiang Zhu and Devis Tuia and Lichao Mou and Gui-Song Xia and Liangpei Zhang and Feng Xu and Friedrich Fraundorfer},
journal= {arXiv preprint arXiv:1710.03959},
year = {2018}
}
Comments
Accepted for publication IEEE Geoscience and Remote Sensing Magazine