Discriminant Neighborhood Preserving Projections Using L1-Norm Maximization for SAR Target Recognition

In this paper, a novel method named discriminant neighborhood preserving projections using L1-norm maximization (DNPP-L1) is developed for Synthetic Aperture Radar (SAR) target recognition. The proposed method can preserve the local geometry information from raw high-dimension data and utilize useful class discriminant information to improve the performance of target recognition effectively. The proposed DNPP-L1 is based on L1 norm distance metric, which is very robust for SAR images target with noise. Experimental results on MSTAR database demonstrate the effectiveness of the proposed method.