Multisource classification using ICM and Dempster-Shafer theory

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 e...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2002-04, Vol.51 (2), p.277-281
Main Authors: Foucher, S., Germain, M., Boucher, J.-M., Benie, G.B.
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Bayesian methods
Classification
Classification algorithms
Decisions
Evidential reasoning
Fuses
Image processing
Instrumentation
Laser radar
Mathematical model
Optical sensors
Pixel
Radar imaging
Remote sensing
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Summary: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.
ISSN:0018-9456
1557-9662
DOI:10.1109/19.997824