Imagery Unsupervised Segmentation Based on GMLR and MMARP Model
基于广义多分辨似然比和混合多尺度自回归预报模型的图像无监督分割

A generalized multiresolution likelihood ratio(GMLR)is defined,then the GMLR test is obtained.The GMLR has the characteristic that can fuse several features which describe different properties,and it can increase distinction between different source outputs,so it is more precise to make a decision.In SAR imagery segmentation,in order to obtain unsupervised segmentation,an efficient mixture multiscale autoregressive prediction(MMARP)model is applied to estimate the parameters of null hypothesis and alternative hypothesis in the GMLR.Finally we classify each individual pixel based on a test window.The method compared with recent competing methods,demonstrating that our method performs better.