Title :
Multisource classification using ICM and Dempster-Shafer theory
Author :
Foucher, Samuel ; Germain, Mickaël ; Boucher, Jean-Marc ; Bénié, Goze Bertin
Author_Institution :
Centre d´´Applications et de Recherche en Teledetection, Sherbrooke Univ., Que., Canada
fDate :
4/1/2002 12:00:00 AM
Abstract :
We propose to use evidential reasoning in order to relax Bayesian decisions given by a Markovian classification algorithm, the multiscale iterated conditional mode (ICM) algorithm. The Dempster-Shafer rule of combination enables us to fuse decisions in a local spatial neighborhood which we further extend to be multisource. This approach enables us to more directly fuse information. Application to the classification of very noisy images produces interesting results
Keywords :
Bayes methods; Markov processes; image classification; iterative methods; radar imaging; sensor fusion; uncertainty handling; Bayesian decisions; Dempster-Shafer combination rule; Dempster-Shafer theory; ICM; Markovian classification algorithm; data fusion; decision fusion; evidential reasoning; multiscale iterated conditional mode algorithm; multisource classification; multisource local spatial neighborhood; noisy image classification; radar image classification; remote sensing; Bayesian methods; Classification algorithms; Fuses; Image processing; Laser radar; Mathematical model; Optical sensors; Pixel; Radar imaging; Remote sensing;
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on
