Bayesian classification of multivariate image after MAP reconstruction of noisy channels
Presents a supervised Bayesian classifier that makes use of both spectral signatures and spatial interactions after the preprocessing of clean noisy channels. The authors apply the Markov random field model at both preprocessing and classification stages. They perform the optimization using either c...
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| creator | Yonhong Jhung Swain, P.H. |
| description | Presents a supervised Bayesian classifier that makes use of both spectral signatures and spatial interactions after the preprocessing of clean noisy channels. The authors apply the Markov random field model at both preprocessing and classification stages. They perform the optimization using either coordinate descent or iterated conditional mode. The estimation of filter parameters is accomplished by referring to adjacent channels that have higher signal-to-noise ratio.< > |
| doi_str_mv | 10.1109/SSST.1994.287840 |
| format | Conference Proceeding |
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| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0094-2898 |
| ispartof | Proceedings of 26th Southeastern Symposium on System Theory, 1994, p.422-426 |
| issn | 0094-2898 2161-8135 |
| language | eng |
| recordid | cdi_ieee_primary_287840 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bayesian methods Cleaning Filters Image reconstruction Markov random fields Parameter estimation Remote sensing Signal to noise ratio Stochastic processes Working environment noise |
| title | Bayesian classification of multivariate image after MAP reconstruction of noisy channels |
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