Title :
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
fDate :
6/21/1905 12:00:00 AM
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;
Conference_Titel :
Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on
Conference_Location :
Kobe
Print_ISBN :
0-7803-5467-2
DOI :
10.1109/ICIP.1999.821579
