Name
Abstract | ||
|---|---|---|
Label information plays an important role in a supervised hyperspectral image classification problem. However, current classification methods all ignore an important and inevitable problem-labels may be corrupted and collecting clean labels for training samples is difficult and often impractical. Therefore, how to learn from the database with noisy labels is a problem of great practical importance... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TGRS.2018.2861992 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | DocType | Volume |
Hyperspectral imaging,Noise measurement,Training,Databases,Noise level,Radio frequency | Journal | 57 |
Issue | ISSN | Citations |
2 | 0196-2892 | 9 |
PageRank | References | Authors |
0.48 | 44 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junjun Jiang | 1 | 1138 | 74.49 |
| Jiayi Ma | 2 | 1302 | 65.86 |
| Zheng Wang | 3 | 352 | 36.33 |
| Chen Chen | 4 | 997 | 50.53 |
| Xianming Liu | 5 | 461 | 47.55 |