Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
Mississippi State University · University of Malaya
Abstract
In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be…
Citation impact
- FWCI
- 40.51
- Percentile
- 100%
- References
- 395
Authors
3- JEJohn E. BallCorresponding
Mississippi State University
- DTDerek T. Anderson
Mississippi State University
- CSChee Seng Chan
University of Malaya
Topics & keywords
- Deep learning
- Focus (optics)
- Field (mathematics)
- Big data
- Transfer of learning
- Deep neural networks
- Artificial neural network