LAUSR

Locality preserving projections (LPP) can preserve the local structure of the datasets effectively. However, it is not capable of separating the samples that are close to each other in the high-dimensional space but belong to different classes. Focusing on the problem, a class-dependent structure preserving projections (CDSPP) algorithm is proposed in this paper to realize synthetic aperture radar (SAR) target configuration recognition. The class information is embedded into the LPP model, and the similarity matrix and the difference matrix are constructed according to the class information. The similarity matrix is utilized to preserve the local structure of the samples belong to the same class, which makes the samples with the same class become more compact after feature extraction. And the difference matrix is utilized to separate the samples that are close to each other in the high-dimensional space but belong to different classes. Target aspect angle sensitivity of SAR images can be eased by using the proposed CDSPP algorithm. Experiments are conducted on the moving and stationary target acquisition and recognition database. The results verify the effectiveness of the proposed algorithm, and comparisons with other algorithms further prove its advantage.