Mine Detection in Averaged Multilook SAR Imagery Using Change Detection Methods

摘要

Several methods for change detection in a pair of multi-look synthetic aperture radar (SAR) images of the same scene are implemented and compare for mine detection. Among the simple methods that are implemented are differencing, Euclidean distance, and image 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, since SAR images are illumination invariant. However, the presence of highly correlated speckle noise, misregistration errors, and nonlinear variations in SAR images cause us to seek more sophisticated methods of change detection in order to obtain more favorable results. Therefore, we implemented methods which incorporate second order statistical calculations in making a change decision in efforts to mitigate false alarms arising from the aforementioned causes. These methods include a Wiener prediction-based method, Mahalanobis distance measure and subspace projection method. We compare the performance of these methods using multi-look SAR images containing several targets (mines). We present results in the form of receiver operating characteristics (ROC) curves.