Abstract: For synthetic aperture radar(SAR) images with the characteristics of complex texture,large brightness range and vague bridge boundary,a method of unsupervised SAR image segmentation based on multi-features was presented.First of all,fcatures of the local moments and the statistics(contrast,correlation,entropy,homogcneity) of gray level cooccurrence matrix were extracted. Secondly,the dimensional reduction operation by principal component analysis(PCA)was applied to these extracted features in order to obtain 2-dimensional features with adequate category information. Finally,pixels with 2-D feature information were automatically clustered by the Mean Shift method. As the Mean Shift clustering method needn't provide the number of cluster, this processing is an unsupervised process of automatic segmentation. Composite image with 13rodatz textures and SAR images were tested in segmenting experiments and the re- sups demonstrate the method can achieve more accurate segmentation than other two methods in which only the gray level co-occurrence matrix or moments are employed.

Key words: SAR images, Tcxturc segmentation, Multi-features, Mcan shift