Attribute Scattering Center Based Algorithm for Target Feature Extraction in Synthetic Aperture Radar Images

Target feature extraction in synthetic aperture radar images is a critical aspect of target recognition. However, the presence of noise in target characteristics complicates the task of improving the recognition rate. In this study, we propose a feature extraction algorithm centered around attribute scattering centers. Initially, the watershed algorithm is employed to segment and extract the target region in the synthetic aperture radar image, thereby reducing the computational cost. Subsequently, the characteristic parameters of the target scattering centers are obtained using the attribute scattering center model. Finally, the orthogonal matching pursuit algorithm is applied to complete the extraction of scattering centers. Our results demonstrate that this approach significantly improves the efficiency and accuracy of radar image scattering center extraction. Numerous experiments performed on the MSTAR database attest to the efficacy of the proposed synthetic aperture radar image target scattering center feature extraction method, achieving an average recognition accuracy of 99.31% for three types of targets in the MSTAR database.