Name
Title | ||
|---|---|---|
Attention-Aware Pseudo-3-D Convolutional Neural Network for Hyperspectral Image Classification |
Abstract | ||
|---|---|---|
Convolutional neural networks (CNNs) have been applied for hyperspectral image classification recently. Among this class of deep models, 3-D CNN has been shown to be more effective by learning discriminative features from abundant spectral signatures and spatial contexts in hyperspectral imagery (HSI). However, by simply imposing 3-D CNN to HSI, a large amount of initial information might be lost ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TGRS.2020.3038212 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | DocType | Volume |
Feature extraction,Solid modeling,Pipelines,Hyperspectral imaging,Convolution,Task analysis,Neural networks | Journal | 59 |
Issue | ISSN | Citations |
9 | 0196-2892 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianzhe Lin | 1 | 141 | 8.50 |
| Lichao Mou | 2 | 254 | 25.35 |
| Xiao Xiang Zhu | 3 | 30 | 8.56 |
| Xiangyang Ji | 4 | 533 | 73.14 |
| Z. Jane Wang | 5 | 4 | 1.09 |