Multiview Features Centers Sample Expansion for SAR Image Classification

Due to the target's radar cross Section (RCS) changes with viewing angle and it is difficult to obtain target's synthetic aperture radar (SAR) images of all the viewing angles, the training samples of SAR target recognition are always incomplete in the dimension of viewing angle. Aiming to solve the problem of views lacking in SAR image training samples, this letter proposes a multiview feature center (MVFC) sample expansion method. It is based on finding the angle-sensitive combination feature center of adjacent views. Through extracting combination features, it changes the image samples into feature dimension. Based on the correlation analysis, each sample's similar samples could be found, and their equivalent centers are used as new sample features. By adding these centers, the sample amount could be doubled in feature dimension. At last, classifier could be trained by using the novel training sample to get better performance. This method transformed the image sample expansion problem into the feature sample expansion and used the multiview equivalent center to double the effective training sample. Experiments based on the moving and stationary target acquisition and recognition (MSTAR) dataset showed that the proposed method has higher recognition accuracy and robustness.