STRE-Diffusion: Few-Shot Spaceborne ISAR Target Recognition via Spatial–Time Relationship Estimation and Improved Diffusion Model

In spaceborne inverse synthetic aperture radar (ISAR) space target recognition, the high-speed relative motion restricts the observation time, leading to insufficient and low-diversity ISAR datasets and reduced recognition accuracy. In existing methods, the dataset augmentation can be achieved by interpolating the azimuth angle of ISAR images obtained in the observation region. However, they lack the capability to extrapolate images in the nonobservation region, limiting the effectiveness in enhancing the angular diversity of the dataset. In this article, a few-shot spaceborne ISAR target recognition method via spatial–time relationship (STR) estimation and improved diffusion network is proposed. The proposed method incorporates the STR into ISAR images prediction, which not only provides additional information for target motion modeling but also enhances predicted image details. First, visual-domain motion cues are extracted via optical flow estimation (OFE). Next, STR is estimated using the optical flow of strong scatterers. Subsequently, a motion cues extrapolation denoising (MCED) module is employed to extrapolate the cross-domain motion cues to the future through an inverse diffusion process. In the MCED, a lightweight spatial–time bilevel routing transformer is proposed for spatial–time feature fusion and alignment. Then, the extrapolated motion cues are mapped back to the image domain to obtain the predicted images. Finally, the predicted images are used to construct the extended dataset, which is applied to few-shot recognition. Experimental results show that the predicted ISAR images generated by the proposed method have higher authenticity. The few-shot recognition performance based on the extended dataset has been significantly improved.