SAR target configuration recognition using Locality Preserving Projections

Because of the special imaging mechanism of Synthetic Aperture Radar (SAR) and the existence of the speckle noise, SAR target configuration recognition has been a hard task in SAR target recognition. We propose a method of SAR target configuration recognition by using the Locality Preserving Projections (LPP), which is a subspace analytical method based on manifold learning. The proposed method extracts features from SAR images in the manifold space which is more suitable for the real SAR images than the Euclidean space. The feature extraction method by LPP not only preserves the global topology structure, but also captures the local information of the target with different configurations. Experimental results on MSTAR datasets suggest that the proposed method can provide a higher recognition rate in SAR target configuration recognition.