Abstract
The exploitation of multi-view synthetic aperture radar (SAR) images can effectively improve the performance of target recognition. However, due to the various extended operating conditions (EOCs) in practical applications, some of the collected views may not be discriminative enough for target recognition. Therefore, each of the input views should be examined before being passed through to multi-view recognition. This paper proposes a novel structure for multi-view SAR target recognition. The multi-view images are first classified by sparse representation-based classification (SRC). Based on the output residuals, a reliability level is calculated to evaluate the effectiveness of a certain view for multi-view recognition. Meanwhile, the support samples for each view selected by SRC collaborate to construct an enhanced local dictionary. Then, the selected views are classified by joint sparse representation (JSR) based on the enhanced local dictionary for target recognition. The proposed method can eliminate invalid views for target recognition while enhancing the representation capability of JSR. Therefore, the individual discriminability of each valid view as well as the inner correlation among all of the selected views can be exploited for robust target recognition. Experiments are conducted on the moving and stationary target acquisition recognition (MSTAR) dataset to demonstrate the validity of the proposed method.
Highlights
Automatic target recognition (ATR) from synthetic aperture radar (SAR) images has important meanings for its pervasive applications in both the military and civil fields [1]
The results demonstrate that use of multiple SAR images can significantly improve the ATR algorithm’s performance even with only two or three SAR views
Zhang et al [20] apply joint sparse representation (JSR) to multi-view SAR ATR, which can exploit the inner correlations among different views
Summary
Automatic target recognition (ATR) from synthetic aperture radar (SAR) images has important meanings for its pervasive applications in both the military and civil fields [1]. Huan et al [19] propose a parallel decision fusion strategy for SAR target recognition using multi-aspect SAR images based on SVM. Zhang et al [20] apply joint sparse representation (JSR) to multi-view SAR ATR, which can exploit the inner correlations among different views. It is assumed that the multiple views of the same target share a similar sparse pattern over the same dictionary while the values of the coefficients corresponding to the same atom may be different for each input view This can be achieved by solving the following optimization problem with 0\ 2 mixed-norm regularization as.
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