## 1 Introduction  
Target classification from optical satellite images is an essential task in remote sensing, which is implemented in various applications to identify geospatial objects such as airplanes, buildings, and ships [1; 2; 3]. Optical satellite images have been extensively used in the maritime domain owing to satellites' capabilities to cover large areas very rapidly, provide synoptic views and high-resolution spatial details, and obtain multi-spectral information. These capabilities are used to produce valuable information for marine applications such as fishery [4], water clarity [5], shoreline extraction [6], and habitat mapping [7]. Optical satellite images are effectively used for patrolling the sea against illegal activities and monitoring maritime traffic [8]. Moreover, their broad coverage in near real-time with high spatial details makes optical satellites feasible for defense applications, such as automated detection of different targets [9; 10]. Parallel to the progress in optical satellite technology and image processing algorithms, the outputs of the studies have evolved from detection through further stages [11; 12]. Researchers have focused on the methods and algorithm development for monitoring the oceans since the first Landsat satellite launched in 1972. [PERSON] and [PERSON] performed one of the preliminary studies on vessel detection using optical satellite imagery [13]. In early studies, defining a vessel from a satellite image was expressed as a classification. However, the classification of ships requires further stages like Detection, Localization, Recognition, and Identification (DLRI). Each stage has to be studied in detail according to the application requirements. Previous research focused on different aspects of the ship detection and classification problem. Researchers applied different levels of preprocessing methodologies in previous studies, such as background and foreground segmentation (sea, lake, or river where the ship is located) using the Geographic Information System [14], Digital Elevation data [2], statistical methods [15], and deep learning methods [16]. In addition, there are studies on eliminating effects like clouds and haze [14] or minimizing the impacts of ocean waves [17]. While some studies aimed to detect ships in the open ocean [18] or insbore [2; 19; 20; 21], some studies went further and classified the ships according to different levels [22; 23]. The dataset's quality and extent (volume) are among the most critical factors in determining the success of various studies. Some researchers used open-source datasets (Table 1), and some generated custom datasets [19; 22; 24] for their implementations. Furthermore, some datasets include satellite images obtained from various sensors to differentiate the objects in different levels of detail. Two previous research studies used Sentinel-2 images with 10 m spatial resolution, which are open-source data but contain some limitations in high spatial detail levels [25; 26]. On the other hand, some of the studies used very high resolution satellite images with a spatial resolution of 1 m or better [27; 28]. Moreover, the spectral bands used in different applications vary depending on the sensor characteristics. While many studies concentrate on Red-Green-Blue bands, or their pan-sharpened versions, some other studies benefited from NIR [29], SWIR [30], and thermal bands [31]. Different investigations have been conducted on various ship sizes regarding detectable object sizes. According to [PERSON] et al., even though some studies define the minimum size of the ship to be detected as between 2 and 150 m, most studies don't specify any limits [10]. Aside from these considerations, the theoretical approaches for DLRI stages have evolved. [PERSON] and [PERSON] employed fundamental statistical image processing methods in one of the first investigations in this area [13]. Today, numerous studies on different levels of the ship DLRI process are based on optical satellite images.