Name
Abstract | ||
|---|---|---|
Recently, the sparse representation-based classification (SRC) methods have been successfully used for the classification of hyperspectral imagery, which relies on the underlying assumption that a hyperspectral pixel can be sparsely represented by a linear combination of a few training samples among the whole training dictionary. However, the SRC-based methods ignore the sparse representation resi... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LGRS.2016.2532380 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Training,Hyperspectral imaging,Robustness,Matching pursuit algorithms,Optimization | Hyperspectral image classification,Matching pursuit,Computer vision,Linear combination,Pattern recognition,Sparse approximation,Outlier,Hyperspectral imaging,Robustness (computer science),Artificial intelligence,Pixel,Mathematics | Journal |
Volume | Issue | ISSN |
13 | 5 | 1545-598X |
Citations | PageRank | References |
17 | 0.61 | 17 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| chang li | 1 | 282 | 19.50 |
| Yong Ma | 2 | 135 | 15.45 |
| xiaoguang mei | 3 | 103 | 15.35 |
| Chengyin Liu | 4 | 65 | 3.19 |
| Jiayi Ma | 5 | 1302 | 65.86 |