Multisource Remote Sensing Data Classification Based on Convolutional Neural Network
Beijing University of Chemical Technology · Mississippi State University · +2 more institutions
Abstract
As a list of remotely sensed data sources is available, how to efficiently exploit useful information from multisource data for better Earth observation becomes an interesting but challenging problem. In this paper, the classification fusion of hyperspectral imagery (HSI) and data from other multiple sensors, such as light detection and ranging (LiDAR) data, is investigated with the state-of-the-art deep learning, named the two-branch convolution neural network (CNN). More specific, a two-tunnel CNN framework is first developed to extract spectral-spatial features from HSI; besides, the CNN with cascade block is designed for feature extraction from LiDAR or high-resolution visual image. In the feature fusion…
Citation impact
- FWCI
- 34.42
- Percentile
- 100%
- References
- 66
Authors
6Topics & keywords
- Computer science
- Convolutional neural network
- Artificial intelligence
- Feature extraction
- Hyperspectral imaging
- Pattern recognition (psychology)
- Block (permutation group theory)
- Feature (linguistics)