Name
Title | ||
|---|---|---|
A Deep Network Architecture for Super-Resolution-Aided Hyperspectral Image Classification With Classwise Loss. |
Abstract | ||
|---|---|---|
The supervised deep networks have shown great potential in improving the classification performance. However, training these supervised deep networks is very challenging for hyperspectral image given the fact that usually only a small amount of labeled samples are available. In order to overcome this problem and enhance the discriminative ability of the network, in this paper, we propose a deep ne... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TGRS.2018.2832228 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Hyperspectral imaging,Training,Feature extraction,Image reconstruction,Task analysis,Image resolution | Iterative reconstruction,Computer vision,Task analysis,Convolutional neural network,Network architecture,Hyperspectral imaging,Feature extraction,Artificial intelligence,Image resolution,Discriminative model,Mathematics | Journal |
Volume | Issue | ISSN |
56 | 8 | 0196-2892 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siyuan Hao | 1 | 21 | 5.08 |
| Wei Wang | 2 | 131 | 14.16 |
| Yuanxin Ye | 3 | 12 | 1.88 |
| Enyu Li | 4 | 10 | 1.18 |
| Lorenzo Bruzzone | 5 | 4952 | 387.72 |