SAR Image Change Detection Based on Multiple Kernel K-Means Clustering With Local-Neighborhood Information

Abstract

Performance of the k-means clustering algorithm for synthetic aperture radar (SAR) image change detection is usually worsened by the inherent existence of the speckle noise. Therefore, in this letter, an unsupervised multiple kernel k-means clustering algorithm with local-neighborhood information ( LIMKKM algorithm) is proposed for SAR image change detection. The LIMKKM algorithm contributes in two aspects. First, it fuses various features through a weighted summation kernel by automatically and optimally computing the kernel weights. Here, the intensity and texture features of the ratio image are fused. Second, it incorporates the local-neighborhood information into its clustering objective function for providing strong noise immunity. The LIMKKM change detection algorithm is carried out in a train–test way to lighten the computational burden. Experimental results on real images demonstrate the effectiveness, especially the strong noise immunity, of the LIMKKM method and illustrate that it is suitable for SAR image change detection.