Global Discriminative Information Search and Focus for SAR Target Recognition

Abstract

Deep learning methods have been widely used in the field of synthetic aperture radar (SAR) target recognition. However, given the difficulty in obtaining high-quality SAR images, existing models tend to focus on non-target regions, leading to uncontrollable overfitting phenomena. To cope with such an inherent problem, a novel global discriminative information search and focus (GDI-SF) network is proposed. The proposed framework obtains a holistic and pure description of the target without increasing the extra model parameters and annotations. Specifically, to capture the global description of the target, we employ higherorder self-correlation (HSC) to enhance the interaction among features and elegantly aggregate global target-related information during the training period. In view of the special imaging mechanism and scattering characteristics of SAR images, the images contain complex interference information, which will be coupled with the target features during the feature global interaction and fail to be separated easily. Thus, we constrain the input data to converge to the target region and eliminate the influence of target-irrelevant information from the source input. Under the above losses constraint, purer global discriminative target features are captured to yield more robust and superior recognition results elegantly. Finally, we conduct experiments on the full aspect stationary targets-vehicle (FAST-Vehicle) dataset and SAR aircraft category (SAR-ACD) dataset to verify the superior performance of the proposed method.