Name
Title | ||
|---|---|---|
Unsupervised Manifold-Preserving and Weakly Redundant Band Selection Method for Hyperspectral Imagery. |
Abstract | ||
|---|---|---|
Hyperspectral band selection is of great value to alleviate the curse of dimensionality. For many band selection methods, however, the neglect of bandwise usefulness tends to result in the loss of valuable bands, but the retention of useless ones; consequently, this causes deterioration of the classification performance. In this sense, bandwise significance should be emphasized. To address this is... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TGRS.2019.2944189 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Manifolds,Measurement,Hyperspectral imaging,Redundancy,Optimization,Correlation | Computer vision,Band selection,Hyperspectral imaging,Artificial intelligence,Manifold,Mathematics | Journal |
Volume | Issue | ISSN |
58 | 2 | 0196-2892 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenhong Sui | 1 | 4 | 1.41 |
| chang li | 2 | 282 | 19.50 |
| Jie Feng | 3 | 247 | 20.11 |
| xiaoguang mei | 4 | 103 | 15.35 |