Model-based superpixel segmentation of SAR images

We propose a superpixel segmentation method for synthetic aperture radar (SAR) images. The method uses the SAR image amplitudes and pixels coordinates as features. The feature vectors are modeled statistically by taking into account the SAR image statistics. Nakagami and bivariate Gaussian distributions are used for amplitudes and position vectors, respectively. A finite mixture model (FMM) is proposed for pixel clustering. Learning and clustering steps are performed using posterior distributions. Based on the classification results obtained on real TerraSAR-X image, it is shown that the proposed method is capable of obtaining more accurate superpixels compared to state-of-the-art superpixel segmentation methods.