## 2 Related Works  
Recent research examined a variety of deep learning algorithms for different stages of DLRI. Some of these studies are summarized below.  
Research by [PERSON] et al., was one of the initial ship detection studies conducted with deep learning techniques [27]. They built a cascade structure joining the feature extraction methods to CNN. The study claimed 99.1% precision for offshore ships and 95.9% for inshorse ships. [PERSON] et al., proposed a coarse ship location method by sea/land segmentation and ship/non-ship classification using deep learning based on wavelet coefficients [32]. They used 2000 SPOT-5 satellite images with 5 m resolution, and defined ships, ocean waves, clouds, coastline, and islands as object classes. The accuracy of the method was 97.58%. This study corresponds to the detection stage of our research. [PERSON] and [PERSON] developed the Singular Value Decomposition (SVDNet), which uses CNN structure and a SVD algorithm to detect images from optical satellite images [33]. The results are reasonably good, with a high recall rate. [PERSON] et al., used a CNN-based algorithm to detect and classify ships among ten classes with a dataset generated from 1200 Google Earth images [23]. They achieved 99% detection-localization and 92% classification accuracy. [PERSON] et al., modified Faster RCNN for ship detection-localization [28]. They reached precision values between 92.95% and 97.64% according to the different confidence and intersection over union (IoU) values. [PERSON] et al., used the Deep Residual Dense Network (DRDN) to detect and classify ships [34]. Their classification stage comprised five different non-ship (wave, island, landing stage, cloud, black background) and five different ships (big, medium, small, multiple, with wake) and achieved a 94.21% F1 score. [PERSON] et al., used the BCCT200 dataset for their ship classification study [35]. Their architecture includes multi-features extraction, CNN, and softmax layers. In the last part of the method, they used the decision level fusion approach to vote on five different feature-based branches. The results of this approach achieved 92.5% average classification accuracy for the four classes of BCCT-200. [PERSON], [PERSON], and [PERSON] worked specifically on inshorse ship detection. They worked with the structured sparse representation model (SSRM), Google Earth, HRSC2016, and DOTA datasets, achieving 87.2% precision and 90.2% recall values [36]. [PERSON] et al., emphasized the importance of negative examples in their work and proposed a method without sea-land segmentation [37]. [PERSON] et al. handled the localization and classification separately [38]. They connected the location stage output to the classification input, the same as HieD. [PERSON] et al., proposed a multi-level ship classification dataset called FGSC-23 and a deep learning-based ship classification model: attribute-guided multi-level enhanced feature representation network (AMEFRN) [22]. The FGSC-23 dataset consists of 4080 images collected from Google Earth and the Gaofen-1 satellite. However, each image patch includes only one type of ship class, and the dataset is not designed for ship localization. Due to the existence of non-ship images, the dataset can be used for detection. The overall classification accuracy of AMEFRN in FGSC-23 is 93.58% in the grained level (L3). Our HieD approach and VHRShips dataset are feasible for classification, detection, and localization, unlike AMEFRN and FGSC-23, which are suitable for classification and detection. Although there have been many studies on DLRI [11; 12; 39], there are still plenty of gaps to be filled in developing a practical ship DLRI application.