Mixture model for the segmentation of the InSAR coherence map

In this work, we classify the interferometric SAR (InSAR) coherence map, computed with the second kind statistics (Abdelfattah and Nicolas, 2006a), into three classes using Bayes’ theorem. The segmentation procedure is performed using a mixture modelling of the coherence map. The multimodal density of the mixture comprises three component functions characterizing different land surface categories (lake, bare soil, urban …). This work is an amelioration version of that published by the authors in (Abdelfattah and Nicolas, 2007 and Abdelfattah and Nicolas, 2006b). We test the performance of the proposed mixture model on three different datasets. The results of this study could be used as a supervised learning step for an automatic land cover classification algorithm. This new method classifying the image considering the corresponding InSAR coherence map is particularly powerful for the detection of layover and shadow regions.