With the development of artificial intelligence and synthetic aperture radar (SAR) imaging technology, methods based on deep learning have been gradually applied to SAR image aircraft detection. However, due to the special SAR imaging mechanism, the imaging results of aircraft targets in SAR images do not have clear features and appear as sparse scattering centers. In addition, airports contain strongly scattering buildings and objects, such as corridor bridges, which will bring huge interference to SAR aircraft target detection. To alleviate these problems, we propose an aircraft scattering information extraction and detection model (SIEDM) based on the spatial imaging characteristics of the targets. First, a scattering center feature extraction module is designed to extract the potential scattering centers of the aircraft targets in the SAR images and acquire the overall structural features of the aircraft targets. In addition, the adaptive noise suppression module is proposed to suppress background interference by learning global information. Finally, we conduct a series of experiments on an SAR image aircraft detection dataset. The experimental results show that the mean average precision of the aircraft SIEDM is 95.7%, which is better than other methods, and the proposed modules in this paper are effective.