In this paper, we present an unsupervised change detection method for polarimetric synthetic aperture radar (Pol-SAR) images based on the relaxed Wishart distribution. Most polarimetric change detectors assume the Gaussian-based complex Wishart model for multilook covariance matrices, which is only satisfied for homogeneous areas with fully developed speckle and no texture. Liu et al. recently proposed a new change detection algorithm under the multilook product model (MPM) to describe the heterogeneous clutters. The improvement has come at the expense of higher computational cost since the similarity measure is based on more advanced models accounting for texture, and they contain some mathematical special functions that is difficult to evaluate such similarity measures. In this paper, we will demonstrate the ability of the relaxed Wishart distribution for textured change detection analysis. Change results on simulated and real data demonstrate the effectiveness of the algorithm.