Name
Abstract | ||
|---|---|---|
The quantity and quality of training samples have a great influence on the performance of most hyperspectral image classification approaches. However, in a real scenario, manually annotating a large number of accurate training samples is extremely labor-intensive and time-consuming. In this article, a multilabel training sample augmentation method is proposed. Instead of giving an exact label to e... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TGRS.2019.2962014 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | DocType | Volume |
Training,Hyperspectral imaging,Annotations,Feature extraction,Labeling | Journal | 58 |
Issue | ISSN | Citations |
6 | 0196-2892 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiaobo Hao | 1 | 1 | 3.05 |
| Shutao Li | 2 | 191 | 16.15 |
| Xudong Kang | 3 | 60 | 7.92 |