Multiscale and multisource classification using Dempster-Shafer theory

Author

Fouche, S. ; Bouche, J. -M ; Bénié, G.B.

Author_Institution

Centre d´´Applications et de Recherche en Teledetection, Sherbrooke Univ., Que., Canada

Volume

1

fYear

1999

fDate

6/21/1905 12:00:00 AM

Firstpage

124

Abstract

We propose the use of evidential reasoning in order to relax the Bayesian decisions given by a multiscale Markovian classification algorithm (ICM). The Dempster-Shafer combination rule enables us to fuse decisions in a local spatial neighbourhood which we further extend to be multiscale and multisource. This approach enables us to more directly fuse multiscale information. Application to the classification of very noisy radar images has produced interesting results

Keywords

belief maintenance; case-based reasoning; image classification; radar imaging; uncertainty handling; Bayesian decisions; Dempster-Shafer combination rule; Dempster-Shafer theory; evidential reasoning; image fusion; image processing; merging images; multiscale Markovian classification algorithm; multiscale classification; multisource classification; noisy radar images; Bayesian methods; Electronic mail; Fuses; Image processing; Laser radar; Low pass filters; Mathematical model; Merging; Radar applications; Radar imaging;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on

Conference_Location

Kobe

Print_ISBN

0-7803-5467-2

Type

conf

DOI

10.1109/ICIP.1999.821579

Filename

821579