Change Detection Based on a Transductive Inference in SAR Images

Abstract

As the change detection based on Synthetic Aperture Radar (SAR) images that are difficult and very limited to acquire labeled samples are of low detection rate and high error rate, Thus a progressive transductive SVM algorithm based on original feature space for unsupervised change detection of SAR images is proposed. The pseudo-training set of the difference image is obtained using K-means clustering method without any prior information; Starting from these initial seeds, the progressive transductive SVM performs change detection in the original multitemporal feature space by gradually considering unlabeled patterns in the definition of the decision boundary between changed and unchanged pixels according to a transductive inference algorithm. Using dynamic region labeling rule, the algorithm not only achieves its rules of progressive labeling and dynamic adjusting, but also raises its speed at the same time. Experimental results obtained on different multitemporal SAR images show that, transductive inference algorithm that extract the information of unlabeled patterns improve the SVM classifier accuracy. These results confirm the effectiveness of the proposed approach.