Unsupervised Classification of Synthetic Aperture Radar Imagery Using a Bootstrap Version of the Generalized Mixture Expectation Maximization Algorithm

In this work, we propose a bootstrapped generalized mixture estimation algorithm for synthetic aperture radar image segmentation. The Bootstrap sampling reduces the dependence effect of pixels in real images, and reduces segmentation time. Given an original image, we randomly select small representative set of pixels. Then, a generalized expectation maximization algorithm based on optimal Bootstrap sample is released for mixture identification. The generalized aspect comes from the use of distributions from the Pearson system. We validate the proposed algorithm on the classification of SAR images. The results we obtain show that the bootstrap sampling method yield the same accuracy and robustness of image classification as the basic algorithm while reducing time computing. This fact make possible the integration of such technique in real time applications.