Name
Title | ||
|---|---|---|
RMCNet: Random Multiscale Convolutional Network for Hyperspectral Image Classification |
Abstract | ||
|---|---|---|
To address the limitation of the high-dimensionality features and single spatial scale in the spectral–spatial classification of hyperspectral image (HSI), we propose a random multiscale convolutional network (RMCNet) that combines a multiscale dimensionality reduction module (MDRM) and the RMCNet for improving classification accuracy. The MDRM is based on multiscale superpixel segmentations, whic... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LGRS.2020.3007433 | IEEE Geoscience and Remote Sensing Letters |
Keywords | DocType | Volume |
Feature extraction,Convolution,Dimensionality reduction,Principal component analysis,Kernel,Training,Hyperspectral sensors | Journal | 18 |
Issue | ISSN | Citations |
10 | 1545-598X | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tian Zhang | 1 | 0 | 0.34 |
| Jun Wang | 2 | 3 | 3.42 |
| Erlei Zhang | 3 | 1 | 1.36 |
| Kai Yu | 4 | 1 | 1.70 |
| Yongqin Zhang | 5 | 0 | 0.34 |
| Jinye Peng | 6 | 284 | 40.93 |