Unsupervised Bayesian segmentation of SAR images using the Pearson system distributions
Hans-Christoph Quelle, Yves Delignon, A. Marzouki · 2002
The authors' work deals with the unsupervised segmentation of radar images. Usually the marginal distribution of each class for SAR image segmentation is supposed Gaussian or Gamma. The authors propose the use of different marginal distributions in order to improve the fitness of the statistic model with the data. The distributions grouped in the Pearson system provide an approximation to a wide variety of observed distributions like in radar image of the sea, ice, etc. The mixture of distributions which characterizes the statistic of the image is estimated by the SEM algorithm and the segmentation is Bayesian. The algorithm obtained is tested on a synthetic image and also applied to the segmentation of real SEASAT scene.>