An expectation-maximization approach assisted by dempster-shafer theory and its application to sonar image segmentation

In this paper we deal with an unsupervised segmentation approach for images given by a synthetic aperture sonar (SAS). The images with objects are segmented into highlight, background and shadow. Since the shape features are extracted from these segmented images, correctness and precision of the segmentation are highly required. We improve the expectation-maximization (EM) methods of Sanjay-Gopal et al. by using the gamma mixture model. Moreover an intermediate step (I-step) based on Dempster-Shafer theory (DST) is introduced between the E- and M-steps of the EM to consider the pixel spatial dependency. Finally, numerical tests are carried out on both synthetic images and SAS images. The results are compared to iterative conditional mode (ICM) and diffused EM (DEM). Our approach provides segmentations with less false alarms and better shape preservation.