Bayesian classification of multivariate image after MAP reconstruction of noisy channels

Author

Jhung, Yonhong ; Swain, Philip H.

Author_Institution

Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA

fYear

1994

fDate

20-22 Mar 1994

Firstpage

422

Lastpage

426

Abstract

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

Keywords

Bayes methods; Markov processes; filtering and prediction theory; image recognition; optimisation; parameter estimation; remote sensing; Bayesian classification; MAP reconstruction; Markov random field model; adjacent channels; classification; coordinate descent; filter parameters; iterated conditional mode; maximum a posteriori estimate; multivariate image; noisy channels; optimization; preprocessing; signal-to-noise ratio; spatial interactions; spectral signatures; Bayesian methods; Cleaning; Filters; Image reconstruction; Markov random fields; Parameter estimation; Remote sensing; Signal to noise ratio; Stochastic processes; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

System Theory, 1994., Proceedings of the 26th Southeastern Symposium on

Conference_Location

Athens, OH

ISSN

0094-2898

Print_ISBN

0-8186-5320-5

Type

conf

DOI

10.1109/SSST.1994.287840

Filename

287840