Name
Abstract | ||
|---|---|---|
This paper presents a new Bayesian model and algorithm for nonlinear unmixing of hyperspectral images. The proposed model represents the pixel reflectances as linear combinations of the endmembers, corrupted by nonlinear (with respect to the endmembers) terms and additive Gaussian noise. Prior knowledge about the problem is embedded in a hierarchical model that describes the dependence structure b... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TCI.2015.2481603 | IEEE Transactions on Computational Imaging |
Keywords | Field | DocType |
Bayes methods,Computational modeling,Estimation,Hyperspectral imaging,Licenses,Markov processes,Joints | Markov process,Bayesian inference,Pattern recognition,Markov random field,Markov model,Marginal likelihood,Artificial intelligence,Hidden Markov model,Bayes estimator,Mathematics,Bayesian probability | Journal |
Volume | Issue | ISSN |
1 | 3 | 2573-0436 |
Citations | PageRank | References |
10 | 0.47 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoann Altmann | 1 | 229 | 22.58 |
| Marcelo Pereyra | 2 | 142 | 16.00 |
| Stephen McLaughlin | 3 | 464 | 43.14 |