Name
Abstract | ||
|---|---|---|
Spectral unmixing is an important technique for hyperspectral data exploitation. In order to solve the unmixing problem using a collection of previously available spectral signatures (i.e., a spectral library), sparse unmixing aims at finding the optimal subset of endmembers to represent the pixels in a hyperspectral image. The classic collaborative unmixing globally assumes that all pixels in a h... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LGRS.2016.2527782 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Collaboration,Hyperspectral imaging,Libraries,Image color analysis,Optimization,Mixture models | Spatial analysis,Endmember,Data set,Remote sensing,Artificial intelligence,Sparse regression,Computer vision,Pattern recognition,Hyperspectral imaging,Pixel,Spectral signature,Mixture model,Mathematics | Journal |
Volume | Issue | ISSN |
13 | 5 | 1545-598X |
Citations | PageRank | References |
2 | 0.36 | 21 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaoquan Zhang | 1 | 4 | 2.08 |
| Jun Li | 2 | 1360 | 97.59 |
| Kai Liu | 3 | 29 | 6.95 |
| Chengzhi Deng | 4 | 37 | 6.45 |
| Lin Liu | 5 | 150 | 26.85 |
| Antonio Plaza | 6 | 3475 | 262.63 |