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Evaluation of different SAR change detectors based on vehicle recognition with MSTAR data set

This letter presents an approach to evaluate the performance of synthetic aperture radar (SAR) change detectors based on target recognition with the moving and stationary target acquisition and recognition (MSTAR) data set. The goal of this study is to develop an assessment method to objectively evaluate the performance of different change detectors. This approach utilizes the minimal differences between the residual data generated by four change detectors to recognize the vehicle types. By comparisons of the recognition rate and time needed to complete the recognition of MSTAR data set, the performance of each change detector can be objectively assessed and a convictive conclusion can be drawn on which change detector is better. The four involved change detectors are the difference change detector (DiCD), ratio change detector, log-ratio change detector and likelihood ratio change detector (LiRCD), respectively. The experimental results show that the LiRCD is with the highest recognition rate, whereas the DiCD is with the lowest recognition rate, which indicates the LiRCD is more appropriate than the other three SAR change detectors in the changed information preservation and speckle noise suppression.