# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### DL-Based Applications for Optical Imagery  
Applications for optical satellite imagery are common in the field of ship and wake detection, due to an easier interpretability than their radar-based counterparts, as well as due to the ability to use the spectral information from multiple frequencies in the near and far infrared. On this point, while in the literature there is no complete review on the topic of wake detection using DL-based methods, it is important to mention a useful review by [PERSON] et al. [12], which compiles some interesting information related to hull and wake detection methods based on infrared remote sensing. The first interesting example of wake detection in optical satellite imagery came from [PERSON] et al. [10]. Their focus was on developing an end-to-end detector named WakeNet, based on CNNs. One innovative idea to come from WakeNet is the addition of a specific head (see Section 3.1) for regression of the wake tip coordinate and Kelvin arm direction. Moreover, they applied a spectral and a multi-scale attention module (MSAM) which exploits the X-shaped wake spectral pattern. In particular, WakeNet is a single-stage anchor-based object detection network composed of four modules:  
1. A backbone CNN for feature extraction. 2. An FPN with a MSAM that enhances the contextual spatial relevance of feature maps at different scales. 3. Classic heads for oriented bounding box (OBB) classification and regression
4. An additional head for landmark regression. Using the property of frequency domain wake images, the backbone for WakeNet integrates a ResNet with a frequency channel attention module. In this way, the network extracts the wake texture features in the image domain, while also learning the wake features in the frequency domain. Apart from the usual classification and BB regression heads, another head is added for landmark regression. In particular, since Kelvin arms are often the most prominent feature of ship wakes in optical imagery, WakeNet regresses the wake tip and the direction of the Kelvin arms. These landmarks also serve to indicate the heading of the ship, which could not be automatically extracted from just the OBBs. Because of this multi-task concept, WakeNet minimizes the multi-task loss function shown in Equation (3) during its training phase:  
\[L(p,t,q)=L_{\text{class}}(p,p^{*})+\lambda_{1}L_{\text{OBB}}(t,t^{*})+\lambda_ {2}L_{\text{landmarks}}(q,q^{*}), \tag{3}\]  
where L\({}_{\text{class}}\), L\({}_{\text{OBB}}\), and L\({}_{\text{landmarks}}\) represent the classification loss, OBB regression loss, and landmark regression loss, respectively. The idea is that the landmarks are mainly used for auxiliary supervision, and thus are weighted less. The landmark head the authors developed also included the use of the RT, to improve the regression of the angles of the line features of the Kelvin arms, as the transformed feature map, and thus the peak detection task was deemed to be more compatible with the nature of a convolutional layers with a rectangular receptive field. To develop WakeNet, [PERSON] et al. [10] gathered their own dataset of about 11,600 optical images, which they called SWIM (Ship Wake Imagery Mass), that is available for researchers. One particular feature of the dataset are its annotations, including both BBs and landmark labels, as well as providing a discrimination for instances with \"difficult\" labels. The dataset contains 14,610 visible waveband satellite and aerial images and can provide up to 15,356 annotated wake instances, with images of spatial resolution from 2.5 m to 0.5 m.  
Another interesting application came from [PERSON] et al. [63]. They proposed an application of a Mask R-CNN [88] for instance segmentation of ship wakes in Sentinel-2 imagery.