Name
Abstract | ||
|---|---|---|
Most traditional approaches classify hyperspectral image (HSI) pixels relying only on the spectral values of the input channels. However, the spatial context around a pixel is also very important and can enhance the classification performance. In order to effectively exploit and fuse both the spatial context and spectral structure, we propose a novel two-stream deep architecture for HSI classifica... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TGRS.2017.2778343 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Feature extraction,Machine learning,Hyperspectral imaging,Training | Computer vision,Convolutional neural network,Communication channel,Feature extraction,Hyperspectral imaging,Pixel,Artificial intelligence,Spatial contextual awareness,Overfitting,Fuse (electrical),Mathematics | Journal |
Volume | Issue | ISSN |
56 | 4 | 0196-2892 |
Citations | PageRank | References |
2 | 0.39 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siyuan Hao | 1 | 21 | 5.08 |
| Wei Wang | 2 | 131 | 14.16 |
| Yuanxin Ye | 3 | 12 | 1.88 |
| Tingyuan Nie | 4 | 2 | 0.73 |
| Lorenzo Bruzzone | 5 | 4952 | 387.72 |