Pyramid Attention Dilated Network for Aircraft Detection in SAR Images

Abstract

Recently, deep learning based methods have been successfully applied in synthetic aperture radar automatic target recognition (SAR ATR) fields. However, due to the effects of the special structures of aircrafts and the complexity of SAR imaging mechanism, detecting aircrafts accurately in SAR images is still challenging. To alleviate this problem, a novel network called pyramid attention dilated network (PADN) is proposed in this letter. The key component of PADN is the dilated attention block (DAB), which is composed of two submodules - multibranch dilated convolution module (MBDCM) and convolution block attention module (CBAM). In our method, MBDCM is used to enhance the relationship among discrete backscattering features of aircrafts. CBAM is employed to refine redundant information and highlight significant features of aircrafts. A well-designed fine-grained feature pyramid is established by combining the two modules reasonably into DAB when building lateral connections. To alleviate class imbalance, focal loss (FL) is employed to train our network. Experiments on a mixed SAR aircraft data set illustrate the efficiency of the proposed method for aircraft detection.