The performance of Synthetic Aperture Radar (SAR) ship detector has been significantly improved with the development of convolutional neural network. However, the issue of effective detection of inshore ships is still a challenging problem. In this paper, we propose a novel one-stage SAR ship detector, called Semantic Attention-Based Network (SANet), which can largely improve the accuracy of ship detection in the inshore scenario without compromising the speed. Specifically, we introduce a semantic attention mechanism, which will highlight the features from the ships area and enhance the detector's classification ability. We train the proposed Semantic Attention Module with focal loss, and assign labels for the attention maps by center sampling. Combined with our anchor assign strategy, our SANet achieves state-of-the-art results on the open SAR Ship Detection Dataset (SSDD).