Abstract
A multiview synthetic aperture radar (SAR) target recognition with discrimination and correlation analysis is proposed in this study. The multiple views are first prescreened by a support vector machine (SVM) to select out those highly discriminative ones. These views are then clustered into several view sets, in which images share high correlations. The joint sparse representation (JSR) is adopted to classify SAR images in each view set, and all the decisions from different view sets are fused using a linear weighting strategy. The proposed method makes more sufficient analysis of the multiview SAR images so the recognition performance can be effectively enhanced. To test the proposed method, experiments are set up based on the moving and stationary target acquisition and recognition (MSTAR) dataset. The results show that the proposed method could achieve superior performance under different situations over some compared methods.
Highlights
As one of the classical problems in remote sensing pattern recognition field, synthetic aperture radar (SAR) target recognition has been researched for decades [1]
A variety of transformation features have been employed in SAR target recognition, e.g., principal component analysis (PCA) [6], non-negative matrix factorization (NMF) [7], monogenic signals [8, 9], etc. e classifiers aim to dig out the discriminability in the extracted features reaching correct decisions on the test samples
A multiview SAR target recognition method is proposed by considering both discrimination and correlation
Summary
As one of the classical problems in remote sensing pattern recognition field, synthetic aperture radar (SAR) target recognition has been researched for decades [1]. A SAR target recognition algorithm was usually conducted on a single-view SAR image via feature extraction and classification processes. Zhang et al first applied joint sparse representation (JSR) to the classification of multiview SAR images, which intended to exploit the inner correlations between different views [19]. Based on the above considerations, this study proposes a multiview SAR target recognition method via discrimination and correlation analysis of multiple views. Ey two classifiers are both effective for SAR target recognition and have complementary merits Their combination could probably help improve the recognition performance. To validate the performance of the proposed method, experiments are set up on the moving and stationary target acquisition and recognition (MSTAR) dataset under different situations. To validate the performance of the proposed method, experiments are set up on the moving and stationary target acquisition and recognition (MSTAR) dataset under different situations. e experimental results confirm the high effectiveness and robustness of the proposed method
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