SAR image segmentation by stochastic complexity minimization with non-parametric noise model
Résumé
We analyse the generalization of a parametric segmentation technique adapted to Gamma distributed SAR images to non parametric noise models. This approach is based on a polygonal grid which can have an arbitrary topology and whose number of regions and regularity of its boundaries are obtained by minimizing the stochastic complexity of a quantified version on Q levels of the image. It thus leads to a criterion without parameters to be tuned by the user and adapted to different noise models. We analyse the influence of the quantization scheme and of the optimization procedure on the quality of the partitioning. We then compare the performance of the proposed approach to the parametric one on synthetic images. Finally we show results obtained on real images and compared with a standard segmentation algorithm of SAR images.