# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### Introduction to DL-Based Methods for Wake Detection
#### 3.3.1. Backbone, Neck, and Heads  
When analyzing detection models, and DL architectures in general, three different parts can usually be identified: these are commonly known as the \"backbone\", \"neck\", and \"head\" [67]. Figure 4 summarizes the basic idea behind this division into a more digestible scheme. The backbone is the part of the detection architecture that extracts features from the input imagery. This denomination is often used interchangeably with the word \"encoder\", since the extraction of feature maps can be considered analogous to a representation problem. Backbones are usually characterized by a CNN, where the input imagery goes through a series of convolution operations and non-linear activation functions to extract significant features and patterns in the data. After each convolution, the data can be downsampled through pooling operations, allowing subsequent convolutional layers to extract higher-level features with higher semantic values but lower spatial resolution. One thing to note is that CNNs are not the only solution to feature extraction, as Transformer-based backbones are also gaining traction [68]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Dataset** & **Image Type** & **Images Characteristics** & **Annotations** & **Links (Last Accessed 27 September 2024)** \\ \hline \multirow{4}{*}{[10]} & \multirow{4}{*}{\begin{tabular}{c} Ship Wake \\ Imagery Mass (SWIM) \\ \end{tabular} } & \multirow{4}{*}{Optical} & 768 \(\times\) 768 pixels Google Earth & \multirow{4}{*}{\begin{tabular}{c} Oriented \\ bounding box \\ examples, up to 15,356 wake \\ instances \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} [[https://www.kaggle](https://www.kaggle)]([https://www.kaggle](https://www.kaggle)). \\ com/datasets/ \\ liitopia/swimship- \\ wake-imagery-mass \\ \end{tabular} } \\  & & & & & \\ \hline \multirow{4}{*}{[65]} & \multirow{4}{*}{\begin{tabular}{c} OpenSARWake \\ \end{tabular} } & \multirow{4}{*}{SAR} & This collection provides 3973 & \multirow{4}{*}{\
\begin{tabular}{c} Oriented \\ bounding box \\ openSARWake \\ \end{tabular} } \\  & & & 1024 \(\times\) 1024 pixels SAR & \multirow{4}{*}{
\begin{tabular}{c} [[https://github.com/](https://github.com/)]([https://github.com/](https://github.com/)) \\ libzfluo/ \\ OpenSARWake \\ \end{tabular} } \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A short list of open high-quality ship wake datasets. The neck takes the feature maps generated at various levels by the backbone as input and combines them in various ways. The combination of maps from different levels serves to enrich feature maps with higher semantic value with the spatial context of higher-resolution maps. The basic idea is that, after all the downsampling applied to obtain these high-level feature maps, the information regarding the position of objects would be partially or completely lost without some form of recombination. A classic neck architecture was introduced with feature pyramid networks (FPNs) [69]. Finally, the heads of the network are tasked with outputting the desired results by feeding combinations of the processed data to specifically designed layers, often fully connected layers (also called \"dense\" layers). The simplest detection models will usually output the coordinates of regressed bounding boxes (BBs), with the predicted class of the detected objects accompanied by some form of confidence score. For semantic segmentation tasks, the outputs will consist of masks related to every class. It is important to note that the previous overview is very general, and that significant differences and exceptions can be found in the literature and in working applications. In the last decade, the field of DL-based CV has significantly evolved, with innovations being presented every day and large investments both in the private and public sectors.