Name
Title | ||
|---|---|---|
Collaborative sparse regression using spatially correlated supports - Application to hyperspectral unmixing |
Abstract | ||
|---|---|---|
This paper presents a new Bayesian collaborative sparse regression method for linear unmixing of hyperspectral images. Our contribution is twofold; first, we propose a new Bayesian model for structured sparse regression in which the supports of the sparse abundance vectors are a priori spatially correlated across pixels (i.e., materials are spatially organized rather than randomly distributed at a... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TIP.2015.2487862 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Bayes methods,Estimation,Collaboration,Licenses,Hyperspectral imaging,Markov processes,Correlation | Bayesian inference,Markov process,Pattern recognition,Markov random field,Hyperspectral imaging,Posterior probability,Pixel,Artificial intelligence,Bayes estimator,Mathematics,Bayesian probability | Journal |
Volume | Issue | ISSN |
24 | 12 | 1057-7149 |
Citations | PageRank | References |
6 | 0.41 | 29 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoann Altmann | 1 | 229 | 22.58 |
| Marcelo Pereyra | 2 | 142 | 16.00 |
| José M. Bioucas-Dias | 3 | 3565 | 173.67 |