SAR target configuration recognition based on the biologically inspired model

How to extract proper features is very important for synthetic aperture radar (SAR) target configuration recognition. However, most of feature extraction methods are hand-designed and usually can not achieve a satisfactory performance. In this paper, we propose a novel method based on the biologically inspired model to extract features automatically from limited data. Specifically, we learn episodic features (containing the key components and their spatial relations) and semantic features (i.e., semantic descriptions of the key components) which are two important types of features for the human cognition process. Episode features are learned through a deep neural network (DNN) and then semantic geometric features of the key components are defined. Moreover, SAR images are very sensitive to aspect angles. Therefore, we use episode features to estimate aspect angles of testing samples for the final recognition. This paper is a preliminary study and the preliminary experimental results on the moving and stationary target automatic recognition (MSTAR) database demonstrate the effectiveness of the proposed method.