This paper presents several methods for change detection in a pair of multi-temporal synthetic aperture radar (SAR) images of the same scene. Several techniques which vary in complexity were implemented and compared. Among the simple methods that were implemented are differencing, Euclidean distance, and image mean ratioing. These methods require minimal processing time, with little computational complexity, and incorporate no statistical information. These methods have demonstrated some degree of accuracy in detecting changes in SAR imagery. However, the presence of highly correlated speckle noise, misregistration errors, and nonlinear variations in SAR images motivated us to seek more sophisticated methods of change detection in order to obtain more favorable results. Therefore, methods were implemented which incorporated second order statistic calculations in making a change decision in efforts to mitigate false alarms arising from the aforementioned causes. Pre-whitened the data was created and then a Wiener prediction-based method, Euclidean distance measure and subspace projection method was implemented. The performance of these methods were compared using multi-look SAR images containing several targets (mines). The results are presented in the form of receiver operating characteristics (ROC) curves.
In this paper we propose a Wiener filter-based change detection algorithm for the detection of mines in Synthetic Aperture Radar (SAR) imagery. By computing second order statistics, the Wiener filter-based method has demonstrated improved performance over Euclidean distance. It is more robust to the presence of highly correlated speckle noise, misregistration errors, and nonlinear variations in the two SAR scenes. These variations may result from differences in the data acquisition systems and varying conditions during the different data collect times. A method very similar to the Mahalanobis distance was also implemented to detect mines in SAR images and has shown similar performance to the Wiener filter-based method. We present results in the form of receiver operating characteristics (ROC) curves, comparing simple Euclidean difference change detection, Mahalanobis difference-based change detection, and the proposed Wiener filter-based change detection in both global and local implementations.