SAR Target Recognition via Information Dissemination Networks

In recent years, deep learning (DL) algorithms have been successfully applied in synthetic aperture radar automatic target recognition (SAR-ATR) owing to its powerful and excellent target feature extraction and representation ability. However, these DL-based models merely exploit the intensity (magnitude) information of SAR target, without fully considering the domain characteristics underlying the SAR images, for example, azimuth, scattering center, phase and so on. To address this issue, this paper proposes a novel information dissemination networks, called IDNets, by both considering the azimuth and strong scatter centers of SAR target in a multi-scale information dissemination mechanism to improve the representation capability of SAR recognition model. Moreover, IDNets introduces a stream-based self-attention (SSA) mechanism to adaptively learn the attention distribution of the multi-streams multi-scale sematic features, further enhancing the performance of SAR-ATR system. Experimental results conducted on the MSTAR dataset demonstrate the effectiveness and superiority of the proposed IDNets compared to the current state-of-the-art DL-based SAR-ATR methods.