Authors:
Gilles Bougenière
1
;
Claude Cariou
1
;
Kacem Chehdi
1
and
Alan Gay
2
Affiliations:
1
TSI2M Laboratory, University of Rennes 1 / ENSSAT, France
;
2
Institute of Grassland and Environmental Research (IGER), United Kingdom
Keyword(s):
Clustering, Classification, Bayesian method, Maximum A Posteriori, Information theory, Remote sensing, Multispectral images.
Related
Ontology
Subjects/Areas/Topics:
Image and Video Processing, Compression and Segmentation
;
Multidimensional Signal Processing
;
Multimedia
;
Multimedia Signal Processing
;
Obstacles
;
Remote Sensing
;
Sensor Networks
;
Telecommunications
Abstract:
In this communication, we propose a novel approach to perform the unsupervised and non parametric clustering of n-D data upon a Bayesian framework. The iterative approach developed is derived from the Classification Expectation-Maximization (CEM) algorithm, in which the parametric modelling of the mixture density is replaced by a non parametric modelling using local kernels, and the posterior probabilities account for the coherence of current clusters through the measure of class-conditional entropies. Applications of this method to synthetic and real data including multispectral images are presented. The classification issues are compared with other recent unsupervised approaches, and we show that our method reaches a more reliable estimation of the number of clusters while providing slightly better rates of correct classification in average.