With the rapid development of deep learning， the object detection network model hsa achieved great success in ship detection from synthetic aperture radar （SAR） images. In order to achieve better results， a large detection network is usually adopted， which requires more computing resources and slower inference. Knowledge distillation can effectively compress the network， but most of them are for image classification. Considering the difference between ships and background in SAR images， this paper proposes a combining local and global distillation method for ship detection. Considering the scattering characteristics of ships in SAR images， slice preprocessing in the amplitude direction is performed on the ship sample data constructing a separate data channel with more significant scattering characteristics， which improves the quality of input data fed to the network. Experimental results based on SAR ship detection dataset （SSDD） show that the proposed approach can effectively reduce the price of the network model and improve the detection performance， achieving 90.7% mAP.