Generalized multiscale Rayleigh likelihood ratio for SAR imagery segmentation

This paper presents a novel method of unsupervised segmentation for synthetic aperture radar (SAR) images. Firstly, multiscale structure inherent in SAR imagery is well captured by a set of multiscale autoregressive (MAR) models, and the MAR prediction follows Rayleigh distribution. Secondly, good parameter estimates of generalized multiscale Rayleigh likelihood ratio (GMLR) can be obtained by estimating several MMARP models using EM algorithm. Thirdly, considering the independence assumption of EM algorithm and reduction of the segmentation time, we present the bootstrap sampling techniques applied above algorithm. Experimental results demonstrate that our algorithm performs fairly well.