Data Fusion and Fuzzy Clustering on Ratio Images for Change Detection in Synthetic Aperture Radar Images

The unsupervised approach to change detection via synthetic aperture radar (SAR) images becomes more and more popular. The three-step procedure is the most widely used procedure, but it does not work well with the Yellow River Estuary dataset obtained by two synthetic aperture radars. The difference of the two radars in imaging techniques causes severe noise, which seriously affects the difference images generated by a single change detector in step two, producing the difference image. To deal with problem, we propose a change detector to fuse the log-ratio (LR) and the mean-ratio (MR) images by a context independent variable behavior (CIVB) operator and can utilize the complement information in two ratio images. In order to validate the effectiveness of the proposed change detector, the change detector will be compared with three other change detectors, namely, the log-ratio (LR), mean-ratio (MR), and the wavelet-fusion (WR) operator, to deal with three datasets with different characteristics. The four operators are applied not only in a widely used three-step procedure but also in a new approach. The experiments show that the false alarms and overall errors of change detection are greatly reduced, and the kappa and KCC are improved a lot. And its superiority can also be observed visually.