A PDF-based SLIC superpixel algorithm for SAR images

The simple linear iterative clustering (SLIC) method is a popular recently proposed superpixel algorithm. However, it may provide bad superpixels for the synthetic aperture radar (SAR) images due to the influence of speckle and large dynamic range of pixel intensity. In this paper, an improved SLIC algorithm for SAR images is proposed by employing the probability density function (PDF) information of SAR image pixel clusters. In this algorithm, a local clustering scheme combining data similarity with spatial proximity is designed, instead of the local k-means clustering used in the standard SLIC method. Moreover, for the post-processing, an edge evolving scheme with a local Bayesian criterion is introduced, instead of the connected components algorithm. In addition, for the precise statistical modeling of SAR images, the generalized gamma distribution (G?D) is exploited. Finally, the superiority of the proposed algorithm is validated on both simulated and real-world SAR images.