Name
Abstract | ||
|---|---|---|
In hyperspectral image classification, jointly using the pixels in an image patch can generally improve the performance. Recently, a new hyperspectral image classification method, which is based on low-rank decomposition model, was proposed by Chen et al. Although this algorithm can achieve state-of-the-art performance and outperform many contemporary classification techniques by jointly classifyi... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LGRS.2016.2633322 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Hyperspectral imaging,Computational modeling,Load modeling,Training,Matrix decomposition,Indexes | Hyperspectral image classification,Computer vision,Chen,Pattern recognition,Hyperspectral imaging,Pixel,Artificial intelligence,Partition (number theory),Sparse regression,Mathematics | Journal |
Volume | Issue | ISSN |
14 | 2 | 1545-598X |
Citations | PageRank | References |
1 | 0.35 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fen Chen | 1 | 81 | 20.55 |
| Peng Zhao | 2 | 14 | 1.81 |
| Ting Feng Tang | 3 | 2 | 0.73 |
| Yan Zhou | 4 | 7 | 1.83 |