Name
Title | ||
|---|---|---|
A Hierarchical Bayesian Model Accounting for Endmember Variability and Abrupt Spectral Changes to Unmix Multitemporal Hyperspectral Images. |
Abstract | ||
|---|---|---|
Hyperspectral unmixing is a blind source separation problem that consists in estimating the reference spectral signatures contained in a hyperspectral image, as well as their relative contribution to each pixel according to a given mixture model. In practice, the process is further complexified by the inherent spectral variability of the observed scene and the possible presence of outliers. More s... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCI.2017.2777484 | IEEE Transactions on Computational Imaging |
Keywords | Field | DocType |
Bayes methods,Hyperspectral imaging,Imaging,Mixture models,Markov processes,Monte Carlo methods | Endmember,Bayesian inference,Markov chain Monte Carlo,Remote sensing,Artificial intelligence,Blind signal separation,Accounting,Computer vision,Outlier,Hyperspectral imaging,Spectral signature,Mathematics,Mixture model | Journal |
Volume | Issue | ISSN |
4 | 1 | 2573-0436 |
Citations | PageRank | References |
1 | 0.36 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pierre-Antoine Thouvenin | 1 | 42 | 2.85 |
| Nicolas Dobigeon | 2 | 2070 | 108.02 |
| Jean-Yves Tourneret | 3 | 1154 | 104.46 |