SAR Image Wake Detection Based on Pseudo-Siamese Structure and Multidomain Feature Fusion

The wake target has garnered increasing attention due to its length, which can be up to ten times that of the ship, and its inclusion of critical navigation information such as heading and speed. However, deep learning methods used in synthetic aperture radar (SAR) image wake detection tasks are limited to analyzing the features of the image itself, overlooking the characteristics of ship wakes in the frequency domain. This letter proposes a network called pseudo-siamese and multidomain feature fusion network (PSMDNet) that is composed of two parallel feature extraction branches. The feature extraction in the frequency domain uses the frequency channel attention network (FcaNet) as the backbone, incorporating an adjacent scale space attention module (ASSAM) to fuse high-level features into low-level features. The time domain uses the residual network (ResNet) as the backbone, incorporating a bidirectional feature channel module (BFCM) to enhance the representation of low-level spatial information. These two parallel branches extract the time- and frequency-domain features from the image to better capture the wake feature information. The proposed ASSAM module calculates weighted coding with context information, thereby selectively aggregating the unique linear spatial features of the wake into the low-level feature map. Verification experiments were conducted on the SAR-WAKE dataset, and the results demonstrate that the proposed method excels in detection accuracy compared with other algorithms, achieving excellent results of 92.71%. Particularly noteworthy is that the positioning and visualization of wake vertex and Kelvin arms are realized by the loss function designed for the wake.