Detecting ships in synthetic aperture radar (SAR) images poses a formidable challenge, primarily attributed to limited observation samples and complex environments. To address this problem, driven by latent similarity between optical and SAR images, we propose a domain-adaptive few-shot detection algorithm for SAR ship detection [single shot multibox detector (SSD)]. The algorithm requires only a few training samples of SAR images and effectively combines them with rich optical images to utilize domain information. First, we develop an efficient plug-and-play distance metric function. This function accurately measures the distances between features from the optical domain and the SAR domain. Second, we design a lossy branching mechanism to effectively utilize SAR domain knowledge. This branching mechanism is driven by the observed latent similarity in domain knowledge distribution between optical and SAR images. In addition, we introduce a dual-stream branching feature alignment extraction network with weight sharing. This network architecture enables better knowledge extraction and sharing between optical and SAR domains. To evaluate our method, we conducted experiments on a newly created dataset, DIOR2SSDD, which is designed for few-shot SAR image ship detections across optical and SAR domains. The experimental results show that under three-, five-, and ten-shot settings, the mean average precision (mAP) of our method can reach 59.2%, 61.2%, and 64.6%, and with only 10% SAR training data, the mAP can reach 89.3%. It indicates that our method can effectively transfer domain knowledge and achieve excellent ship detection performance in SAR images.