Deep learning based target detection method with multi-features in SAR imagery

In view of synthetic aperture radar (SAR) target detection, traditional methods are based on hand-crafted feature extraction and classifier. Besides, deep learning (DL) based methods are research hotspots in recent year. However, their shortcomings cannot be neglected, i.e. detection accuracy of traditional method needs to be improved and DL features are difficult to interpret. To overcome these problems, a target detection method with multi-features in SAR imagery is proposed in this paper. It consists of two parallel sub-channels. DL features and hand-crafted features are extracted in these channels, respectively. Here, convolutional neural network (CNN) model is applied to capture DL features of original SAR images. Deep neural network (NN) is used to further analyze hand-crafted features. Furthermore, two sub-channel features are concatenated together in the main channel. After several layers network processing, fused deep features are extracted. Finally, softmax classifier is applied to discriminate ship target. According to the experiments based on Sentinel-1 SAR data, we can find that the detection performance is improved by the proposed method.