Exploring a Novel Content-Guided High-Resolution SAR Ship Image Generation Method

Deep learning-based synthetic aperture radar (SAR) ship image processing is essential in both civilian and military applications. However, training deep learning models requires a large dataset of high-resolution SAR ship images. Since SAR sensors are not readily available, obtaining an adequate number of such images remains a significant challenge. The SAR image generation technique is a useful way to create SAR ship images. However, many current methods depend only on location maps as an input and do not incorporate guidance for the content of the SAR images, resulting in the production of low-quality images. To tackle these issues, we propose a novel content-guided high-resolution SAR ship image generation method. This approach incorporates SAR image-specific information to enhance the details of the generated images. We achieve this by separately fusing the input source image with both the source and target location maps, using them as guidance to steer the generation process. Additionally, to effectively transfer this guidance to the target SAR image, we introduce a feature transferring module (FTM), which performs affine transformations based on scaling and shifting values learned by a parameter learning module (PLM). Moreover, we design a mask transformer module (MTM) to learn the correlation weight and establish a fine-grained mapping between the source and target SAR images. To train and evaluate the proposed model, we have relabeled the land and sea regions in the HRSID dataset for comparative analysis. Extensive experiments demonstrate that our model performs exceptionally well in the high-resolution SAR image generation task.