## 2 Applied Sensor Engineering in Logistic and Shipping Activities
Synthetic aperture radar (SAR) is an active microwave imaging sensor that can provide high-resolution images under all weather and all light conditions [59; 60]. Consequently, it plays a crucial role in marine monitoring and maritime traffic supervision [61]. Research on generating multiscale high-resolution synthetic aperture radar (SAR) images for ship detection was carried out by [PERSON] et al. [61]. In the study, a high-resolution synthetic aperture radar (SAR) ship detection method was combined with an improved sample generation network, a multiscale Wasserstein auxiliary classifier generative adversarial network (MW-ACGAN), and the Yolo v3 network, to meet the application requirements of high-resolution small ship detection. The result shows that the Multiscale Wasserstein Auxiliary Classifier Generative Adversarial Network (MW-ACGAN) can effectively generate realistic multiclass ship images. The process of developing high-resolution synthetic aperture radar (SAR) ship slices is presented in Figure 3. The amount of ship traffic activity on the Northern Sea Route (NSR) of Russia's northern coast from 2016 to 2019 is illustrated in Figure 4. As can be seen, the total number of ship voyages increased from 2016 to 2019 [62]. Figure 2: A GeoEye-1 optical image of Cascais in Portugal [56]. Synthetic aperture radar (SAR) images still possess several challenges--for example, most existing methods only detect ships with horizontal bounding boxes. Furthermore, ships are frequently arbitrary-oriented and densely arranged in complicated backgrounds. These deficiencies have received attention from researchers such as [PERSON] et al. [63]. A multi-stage rotational region-based network (MSR2N) was proposed by [PERSON] et al. [63] to solve the above problems. Additionally, the MSR2N can reduce background noise and prevent missing detection. The result is shown in Figure 5. As can be seen, as the interval of rotation angles decreases, the detection performance improves. [PERSON] et al. [64] proposed a new detector based on convolution neural networks for multiscale synthetic aperture radar (SAR) ship detection in complex backgrounds. The model was applied to the Gaofen-3 dataset, a high-resolution civil SAR satellite in China, in order to test it. Comparisons of the performance with the SAR ship detection dataset (SSDD) and the proposed method are presented in Figure 6. The results show that the new method has a better performance than that of the SAR ship detection dataset (SSDD). The landmark research on sensor engineering applications in logistic and shipping activities is summarized in Table 2.  
Figure 4: Ship traffic statistics on the Northern Sea Route (NSR) in 2016-2019: (**a**) the total number of voyages and (**b**) the total number of sailing permits for Russia’s Northern Sea Route Administration (NSRA) (redrawn based on data in [PERSON] [62]). Figure 3: The method of creating high-resolution synthetic aperture radar (SAR) ship slices [61]. Figure 5: The experimental results of the multi-stage rotational region-based network (MSR2N): (**a**) results of different methods on the SAR ship detection dataset (SSDD); (**b**) results of rotation angles (MSR2N) (redrawn based on data in [PERSON] et al. [63]). \begin{table}
\begin{tabular}{c c c c c} \hline
**Milestone** & **Author(s)** & **Observational Subject** & **Tools/Instrument** & **Important Remarks** \\ \hline \multirow{6}{*}{2007} & & & & The study shows that Moderate-Resolution \\  & & & & Imaging Spectroradiometer (MODIS) \\ \cline{3-5}  & & & & sensors provided better coverage than \\ \cline{3-5}  & & Ship-based sampling & & Sea-viewing Wide Field-of-view Sensor \\ \cline{1-1}  & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & for evaluation of & High-resolution & (SeaWiFS) or Advanced Very \\ \cline{1-1}  & & stormwater in the & satellite imagery & High-Resolution Radiometer (AVHRR) due to greater spectral, spatial, and particularly, temporal resolution (twice a day), thereby significantly improving information about plume dynamics.  
\end{table}