###### Abstract
[26] proposed method carries out image geometric transformation, such as flipping, rotation, clipping, deformation, and scaling, as well as color transformations, such as denoising, blur, erasure, filling, etc. With the above methods, an image can produce lots of data, and the features of the generated image data are highly similar to the image samples. The GAN (generative adversarial network) algorithm [27] and its improved algorithm [28; 29; 30; 31; 32] have become effective methods for data enhancement in recent years. The simulation data generated in this manner are quite different from the sample data, whereasthey are highly similar to the real data. However, this type of algorithm has its downsides. For example, many samples are employed as training data before the target data are generated. Additionally, the performance of the generated samples is limited and constrained by the training samples [33; 34; 35; 36; 37; 38]. For the above reasons, designing a method to generate ship images for CNN training and testing is necessary. We alternatively used generated simulation ship image data to build special ship image data to address this problem. In general, 3D data are considered to contain more information than 2D data. Therefore, we carried out a geometric transformation to generate image data by projecting the transformed 3D target point set to a fixed 2D plane. This method is suitable for expanding the amount of ship image data. Beside the difficulty of collecting a large amount of training data, the second problem for deep learning applications is the annotation of these image data. Traditional image annotation methods includes the manual and automatic annotation method. Both methods need to detect the contour of the target. The manual method is carried out by eye and experience to identifythe target's contour. In contrast, the automatic method identifies the target's contour using the target recognition algorithms or edge detection algorithms. Manual annotation methods mainly include boundary box annotation, such as Label-img, and peripheral contour annotation, such as Label-me. [PERSON], M. et al. [39] proposed a weighted spatial Fuzzy C-Means (wsFCM) segmentation method that considers the image's spatial information to segment objects and backgrounds in an image. [PERSON], M. et al. [40] proposed a new fuzzy edge detector based on both fuzzy divergence and fuzzy entropy minimization to identify the object's contour in the image. The above two methods need to take each pixel in the image to model the image, and then perform the calculation. It is difficult to achieve the purpose of real-time image target detection using this method. This paper presents an annotation method based on SRS images, which automatically and quickly generates many annotation data. The contributions of this paper are summarized as follows:  
(1) An SRS images building method based on a specific scene is proposed. The simulated target ships are superimposed on the background image's specified position to form SRSimages. (2) This paper presents an automatic annotation method of ship image which quickly generate many annotation data for CNN training and testing.