## 3 RS Applications in Ocean
### Ship Detection (SD)  
SD has a wide variety of environmental, civil, and military applications. For example, the benefits of automatic locating and tracking ships for the civil sector are maritime management, vessel traffic services, safety and rescue, fishery management, and illegal fishery surveillance. Moreover, the main applications in the military sector include naval warfare, battlefield environment assessment, and pirate activity surveillance [313; 314; 315]. RS has a leading role in SD and monitoring because of its several advantages, such as the availability of open-access multitemporal datasets and large area coverage.  
Although various RS datasets have been used for SD (e.g., hyperspectral [316], TIR [317], and UAV [318] imagery), optical, SAR, and HF radar are the most common RS systems for SD [313]. Table 6 summarizes the main advantages and disadvantages of each of these systems for SD.  
SD methods using spaceborne RS (i.e., optical and SAR) datasets generally have three main steps (see Figure 9): (1) ocean-land segmentation, (2) ship candidate extraction, and (3) classification of ship candidates [319; 320; 321]. Since the objective of the corresponding studies is to detect ships in oceans, the first step is separating ocean and land regions. This is usually performed using GIS layers of coastlines. However, with the end-to-end DL SD methods, this step is not necessary anymore. Most of the SD research studies have focused on the second and third steps by developing better features for ship description and False Alarm Rate (FAR) reduction. For example, in the second step, a simple shape analysis is performed to remove obvious FAR and extract Regions of Interest (ROI) that may contain  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RS System (Passive/Active)** & **RS System (Type)** & **Advantage** & **Disadvantage** \\ \hline \multirow{3}{*}{Passive} & \multirow{3}{*}{Optical} & \multirow{3}{*}{Relatively high resolution} & Functional only during the daytime, \\  & & & affected by clouds and weather condition \\ \cline{1-1} \cline{3-4} \multirow{3}{*}{Active} & \multirow{3}{*}{SAR} & Operational in all weather conditions and all times & Speckle noise, difficult interpretation \\  & & Operational in all weather conditions and all times & Lack of data availability due to the limited number of radars \\ \hline \hline \end{tabular}
\end{table}
Table 6: Different RS systems for SD along with their advantages and disadvantages.  
potential ship candidates. In the third step, the ship candidates are classified into ship and nonship classes.  
In the following three subsections, the most commonly used approaches for SD using optical, SAR, and HF radar data are discussed. However, it should be noted that more advanced ML methods, such as DL, have been recently employed for SD with high accuracies. For instance, among many object detection DL methods, the Region-based CNN (RCNN) [322] and its modified versions (e.g., Fast-RCNN [323] and Faster-RCNN [324]) are mostly used for SD. RCNN-based methods involve two major steps: (1) a CNN algorithm extracts the shared feature maps, and the region proposal network algorithm generates candidate regions, including potential ship targets; and (2) the network classifies these proposals into specified classes. DL methods can extract semantic-level features that are robust to varying ship sizes and different ocean conditions, resulting in better performance than traditional methods with human-crafted features and descriptors. However, the main limitation of DL methods is the limited accessibility to sufficient reference sample data [325; 326].