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This letter presents a novel unsupervised change detection approach for multi-temporal synthetic aperture radar (SAR) image. The proposed method applies the semi-supervised Laplacian support vector machine (SVM) to classify the changed areas and the unchanged areas. A pseudo-training set, which is necessary for initializing the SVM, is generated according to the saliency similarity detection. The pseudo-training set is composed by the labelled changed pixels and the labelled unchanged pixels. The Laplacian SVM explores the prior information of the available labelled samples and combines unlabelled samples to enhance its discrimination. Experimental results on several real SAR image data sets confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]