Deep learning in remote sensing: A comprehensive review and list of resources
IEEE geoscience and remote sensing magazine, 2017•ieeexplore.ieee.org
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 …
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 …
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.
ieeexplore.ieee.org