Abstract:

With the existing synthetic aperture radar (SAR) image target recognition algorithm, image preprocessing has to be usually carried out. Preprocessing has a significant impact on the recognition rate. However, the adaptability of the preprocessing algorithm is difficult to be guaranteed. This paper proposes to apply the theory of kernel principal component analysis (KPCA) and sparse representation to the image to be recognited, thus achieving a target recognition result possessing a  high recognition rate with only a few observation data and saving the data storage and computation. This paper describes the basic theory of compressed sensing first, and proposes an SAR image target recognition algorithm based on KPCA and sparse representation. An experiment is carried out with five kinds of SAR targets in the MSTAR database. The simulation results show that this proposed algorithm is still able to recognize the target effectively without prediction of the attitude angle. Compared with other recognition algorithms, it has a higher recognition rate to the image under the noise pollution on an equal basis.