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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Eches, O. Dobigeorv, N. Tourneret, J.-Y. Snoussi, H. |
| Copyright Year | 2011 |
| Description | Author affiliation: University of Toulouse, IRIT/INP-ENSEEIHT/TéSA, 31071 Cedex 7, France (Eches, O.; Dobigeorv, N.; Tourneret, J.-Y.) || University of Technology of Troyes, ICD/LM2S, 10000 Cedex, France (Snoussi, H.) |
| Abstract | This paper studies a variational Bayesian unmixing algorithm for hyperspectral images based on the standard linear mixing model. Each pixel of the image is modeled as a linear combination of endmembers whose corresponding fractions or abundances are estimated by a Bayesian algorithm. This approach requires to define prior distributions for the parameters of interest and the related hyperparameters. After defining appropriate priors for the abundances (uniform priors on the interval (0, 1)), the joint posterior distribution of the model parameters and hyperparameters is derived. The complexity of this distribution is handled by using variational methods that allow the joint distribution of the unknown parameters and hyperparameter to be approximated. Simulation results conducted on synthetic and real data show similar performances than those obtained with a previously published unmixing algorithm based on Markov chain Monte Carlo methods, with a significantly reduced computational cost. |
| Starting Page | 957 |
| Ending Page | 960 |
| File Size | 560997 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457705380 |
| ISSN | 15206149 |
| e-ISBN | 9781457705397 |
| e-ISBN | 9781457705373 |
| DOI | 10.1109/ICASSP.2011.5946564 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-05-22 |
| Publisher Place | Czech Republic |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Bayesian methods Approximation methods Hyperspectral imaging Signal processing algorithms Pixel Approximation algorithms Noise hyperspectral images Bayesian inference variational methods spectral unmixing |
| Content Type | Text |
| Resource Type | Article |
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