LAUSR

Synthetic Aperture Radar (SAR) is a useful tool in marine surveillance. Small targets detection in SAR images especially in nearshore area is a difficult issue. Due to the complex background, there exist a lot of false targets. Therefore, we propose an effective method for small targets detection in SAR images under complex background, which combines the features of SAR images and those of SAR time series. A new neural network which integrates a neighborhood similarity module is constructed to enhance the features of small targets in SAR images. Then, a false alarm suppression method is put forward, which is based on empirical orthogonal functions to extract spatio-temporal features. Compared with other false alarm suppression methods, our proposed method is easily-implemented, highly efficient and in no need of a priori information. Simulation results on real datasets prove the efficiency and effectiveness of our proposed method.