Name
Abstract | ||
|---|---|---|
Hyperspectral sparse unmixing methods estimate abundance of endmembers, assuming spectral library as an overcomplete set of endmembers. In this letter, we present a novel, fast and efficient dictionary pruning approach for hyperspectral unmixing. We quantify the change in the latent structure of data due to augmentation of spectral library element using covariance similarity measure. Since the cov... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LGRS.2018.2888580 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Libraries,Covariance matrices,Hyperspectral imaging,Dictionaries,Measurement,Manifolds | Computer vision,Divergence,Pattern recognition,Similarity measure,Matrix (mathematics),Hyperspectral imaging,Artificial intelligence,Nonlinear manifold,Mathematics,Covariance | Journal |
Volume | Issue | ISSN |
16 | 6 | 1545-598X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samiran Das | 1 | 3 | 2.39 |
| Aurobinda Routray | 2 | 337 | 52.80 |