Deep learning in multimodal remote sensing data fusion: A comprehensive review
Chinese Academy of Sciences · Aerospace Information Research Institute · +6 more institutions
Abstract
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity are readily available nowadays, which renders researchers an opportunity to tackle current geoscience applications in a fresh way. With the joint utilization of EO data, much research on multimodal RS data fusion has made tremendous progress in recent years, yet these developed traditional algorithms inevitably meet the performance bottleneck due to the lack of the ability to comprehensively analyze and interpret strongly heterogeneous data. Hence, this non-negligible limitation further arouses an intense demand for an alternative tool with…
Citation impact
- FWCI
- 60.22
- Percentile
- 100%
- References
- 263
Authors
7- JLJiaxin Li
Chinese Academy of Sciences, Aerospace Information Research Institute, University of Chinese Academy of Sciences
- DHDanfeng Hong
Chinese Academy of Sciences, Aerospace Information Research Institute
- LGLianru GaoCorresponding
Chinese Academy of Sciences, Aerospace Information Research Institute
- JYJing Yao
Chinese Academy of Sciences, Aerospace Information Research Institute
- KZKe Zheng
Liaocheng University, Chinese Academy of Sciences, Aerospace Information Research Institute
Topics & keywords
- Sensor fusion
- Computer science
- Data science
- Bottleneck
- Field (mathematics)
- Deep learning
- Artificial intelligence
- Big data