Change Detection in Synthetic Aperture Radar Images Using Contourlet Based Fusion and Kernel K-Means Clustering

Change detection algorithms play a vital role in overseeing the transformations on the earth surface. Unsupervised change detection has an indispensable role in an immense range of applications like remote sensing, motion detection, environmental monitoring, medical diagnosis, damage assessment, agricultural surveys, surveillance etc. In this paper, a novel method for unsupervised change detection in multitemporal images based on image fusion and kernel K-means clustering is proposed. Here difference image is generated by performing image fusion on meanratio and log-ratio image and for fusion contourlet transform is used. On the difference image generated by collecting the information from mean-ratio and log-ratio image kernel K-means clustering is performed. In kernel K-means clustering, non-linear clustering is performed, as a result the false alarm rate is reduced and accuracy of the clustering process is enhanced. The aggregation of image fusion and kernel K-means clustering is seen to be more effective in detecting the changes than its preexistences.