*3.1. Experimental Datasets*

One of the main reasons for the lack of research on SAR ship recognition in the past has been insufficient data. Now, thanks to the SAR rotation ship detection dataset (SRSDD) released by Lei et al. [22] in 2021, the research on SAR ship detection and classification can be further developed. The images in SRSDD all come from China's GF-3 satellite, which photographed more than 30 ports from five locations. The size of each slice is 1024 × 1024. Table 1 shows more details about SRSDD.

**Table 1.** The basic parameters of SRSDD.


The ships in SRSDD are marked by the rotated bounding box and are divided into six categories, including ore–oil ships, bulk cargo ships, fishing boats, law enforcement ships, dredger ships and container ships. The rotated bounding box and the category of each ship target are given by experts after checking the corresponding SAR image and corresponding optical image. This ensures their authenticity and accuracy. Table 2 shows the number of each category. It can be seen from the table that the number of bulk cargo accounts for most of the total, while the number of law enforcement is almost one tenth of that of bulk cargo.

In addition, considering the problem that the number of offshore scenes is larger than that of inshore scenes in the existing SAR dataset, such as SSDD [21,46] and Gaofen-SSDD [47], SRSDD focuses on taking nearshore scenes during sampling. In SRSDD, inshore scenes account for 63.1%, and offshore scenes account for 36.9%. In order to ensure fairness of the experimental results, our experiment is completely consistent with Ref. [22], that is, 532 pictures are used for training, and 134 pictures are used for testing.

**Table 2.** The number of each ship category in SRSDD.

