  Aircraft target detection in SAR images is a challenging task due to the
discrete scattering points and severe background clutter interference.
Currently, methods with convolution-based or transformer-based paradigms cannot
adequately address these issues. In this letter, we explore diffusion models
for SAR image aircraft target detection for the first time and propose a novel
\underline{Diff}usion-based aircraft target \underline{Det}ection network
\underline{for} \underline{SAR} images (DiffDet4SAR). Specifically, the
proposed DiffDet4SAR yields two main advantages for SAR aircraft target
detection: 1) DiffDet4SAR maps the SAR aircraft target detection task to a
denoising diffusion process of bounding boxes without heuristic anchor size
selection, effectively enabling large variations in aircraft sizes to be
accommodated; and 2) the dedicatedly designed Scattering Feature Enhancement
(SFE) module further reduces the clutter intensity and enhances the target
saliency during inference. Extensive experimental results on the
SAR-AIRcraft-1.0 dataset show that the proposed DiffDet4SAR achieves 88.4\%
mAP$_{50}$, outperforming the state-of-the-art methods by 6\%. Code is
availabel at \href{https://github.com/JoyeZLearning/DiffDet4SAR}.
