# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### DL-Based Applications for SAR Imagery  
SAR-based maritime monitoring has been the subject of fervent study by the scientific community due to its all-weather all-time characteristics. In fact, research on wake detection methods using SAR imagery is much more common than with optical imagery, making use of its characteristics and looking for smart ways to approach the problem that often do not involve DL. However, traditional methods of wake detection in SAR imagery are often based on threshold evaluations and are naturally susceptible to high false alarm rates, not only due to the presence of clutter, but also due the inevitable presence of speckle. In this sense, learning-based methods can help to improve performance, by being more robust against any kind of interfering factor. The first publication analyzed in the context of SAR-based applications came from [PERSON] et al. [61]. This work, published in 2021, represented the first DL approach to specifically detect ship wakes without any a priori knowledge, and without more specific information about the location of the vessel that generated the wake. The authors first introduced the field as a whole, with a complete analysis of the field of DL-based detectors, then deployed several architectures on the same custom SAR wake detection dataset to evaluate the strengths and weaknesses of each. The dataset in question, called SSWD (SAR Ship Wake Dataset) by the authors, includes 261 wake chips extracted from Interferometric Wide (IW) swath Sentinel-1 SAR images with VV polarization and with a pixel resolution of about 10 m \(\times\) 10 m (ground range \(\times\) azimuth), with particular attention paid to obtaining a reasonable statistical distribution of orientations of instances. Polygon annotations were used to overcome the problem of the narrow shape during the random rotations in the data augmentation strategy. The authors selected several object detection architectures, testing 21 combinations of different backbones and necks, including Faster R-CNN [91], Mask R-CNN [88], Cascade Mask R-CNN [92], and RetinaNet [93]. All combinations were tested, starting from transfer learning of pre-trained weights on the ImageNet and COCO benchmark datasets. The best performing model from their evaluation was Cascade Mask R-CNN with a ResNet + FPN backbone, which showed a robust performance in wake detection, despite the challenges posed by coherent speckle-noise. The authors, noting the performance of the Cascade approach, further explored its generalizability by testing its performance on X-band SAR images from TerraSAR-X and COSMO-SkyMed satellites, evaluating its detection capabilities across different sensors, resolutions, and polarization. The model demonstrated strong generalizability when applied to X-band SAR images, indicating its potential versatility across different radar imaging platforms and conditions. An example of the application of the model to X-band SAR is shown in Figure 5.  
As previously stated, the interest in security and surveillance applications regarding ship and wake detection has pushed a significant subset of the field towards lightweight solutions that can offer near-real-time capabilities, as timeliness is highly valued in this context. Thus, [PERSON] et al. [8] focused on developing a lightweight DL-based ship and wake detector trained on Gaofen-3 imagery, which was primarily intended for embedded devices in a military context. In particular, they used a YOLO-like structure with some specific modifications and an attention mechanism. In line with the \"lightweight idea\", their backbone has only three layers, which are also not traditional convolution-batch normalization-activation layers, but combined convolution layers (CBC) specifically developed by the authors for this application. These modules involve the use of squeeze and excitation (SE) modules [94], enabling them to perform dynamic channel-wise feature re-calibration.