Synthetic aperture radar (SAR) is characterized by its all-weather monitoring capabilities and high-resolution imaging. It plays a crucial role in operations such as marine salvage and strategic deployments. However, existing vessel detection technologies face challenges such as occlusion and deformation of targets in multi-scale target detection and significant interference noise in complex scenarios like coastal areas and ports. To address these issues, this paper proposes an algorithm based on YOLOv8 for detecting ship targets in complex backgrounds using SAR images, named DFENet (Denoising and Feature Enhancement Network). First, we design a background suppression and target enhancement module (BSTEM), which aims to suppress noise interference in complex backgrounds. Second, we further propose a feature enhancement attention module (FEAM) to enhance the network’s ability to extract edge and contour features, as well as to improve its dynamic awareness of critical areas. Experiments conducted on public datasets demonstrate the effectiveness and superiority of DFENet. In particular, compared with the benchmark network, the detection accuracy of mAP75 on the SSDD and HRSID is improved by 2.3% and 2.9%, respectively. In summary, DFENet demonstrates excellent performance in scenarios with significant background interference or high demands for positioning accuracy, indicating strong potential for various applications.

Accurate target prior information is an important basis for improving the detection and recognition performance of high-resolution range profiles (HRRPs). To address the insufficient reliability of prior templates in scattering-center-assisted HRRP target recognition based on SAR images, where scattering centers extracted from a single image are susceptible to noise, sidelobes, local scattering fluctuations, and variations in observation azimuth angle, this paper proposes an HRRP target recognition method based on the fusion of scattering-center priors derived from SAR images acquired at adjacent azimuth angles. Two-dimensional scattering centers at different azimuth angles are extracted by combining an adaptive point-spread function (PSF) with the two-dimensional CLEAN algorithm. Amplitude-weighted clustering is then employed to retain stable scattering structures with spatial consistency, thereby constructing a fused scattering center model. Subsequently, the fused model is projected onto the range dimension under different azimuth offsets to establish a one-dimensional scattering center prior template library. Target classification is finally performed using sliding matching based on the maximum correlation coefficient. Same-band, cross-band, and comparative experiments conducted with ground-target data sets demonstrate that the proposed method effectively improves the stability of the prior templates and target recognition performance, while exhibiting reasonable adaptability to variations in observation azimuth angle, noise level, and radar operating frequency band.