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Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they generally use a feature pyramid neck design and large backbone network, which reduces the detection efficiency to some extent. To address these problems, we propose a simple and efficient attention network (SEAN) in this paper, which takes YOLOv5s as the baseline. First, we shallow the depth of the backbone network and introduce a structural re-parameterization technique to increase the feature extraction capability of the backbone. Second, the neck architecture is designed by using a residual dilated module (RDM), a low-level semantic enhancement module (LSEM), and a localization attention module (LAM), substantially reducing the number of parameters and computation of the network. The results on the Gaofen-3 aircraft target dataset show that this method achieves 97.7% AP at a speed of 83.3 FPS on a Tesla M60, exceeding YOLOv5s by 1.3% AP and 8.7 FPS with 40.51% of the parameters and 86.25% of the FLOPs.

A novel PolSAR image speckle reduction algorithm based on a new definition of similarity coefficient is proposed in this paper. Pixels in image are firstly classified into three types by threshold segmentation which is calculated with the similarity features. Then, weighted filtering is applied on the pixels selected according to their types, power features and similarity properties. Experimental results with measured data collected by NASA/JPL AIRSAR system show that the proposed method is more effective than Lee Filter not only in speckle suppression but also in polarimetric properties and structure feature preservation.

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