Abstract
This paper proposes a synthetic aperture radar (SAR) image target recognition method using multiple views and inner correlation analysis. Due to the azimuth sensitivity of SAR images, the inner correlation between multiview images participating in recognition is not stable enough. To this end, the proposed method first clusters multiview SAR images based on image correlation and nonlinear correlation information entropy (NCIE) in order to obtain multiple view sets with strong internal correlations. For each view set, the multitask sparse representation is used to reconstruct the SAR images in it to obtain high-precision reconstructions. Finally, the linear weighting method is used to fuse the reconstruction errors from different view sets and the target category is determined according to the fusion error. In the experiment, the tests are conducted based on the MSTAR dataset, and the results validate the effectiveness of the proposed method.
Highlights
With the continuous enhancement of synthetic aperture radar (SAR) data acquisition capabilities, it has become possible to acquire SAR images of the same target from multiple views, which provides more available information for correct target recognition [1]
Zhang et al first proposed a multiview SAR target recognition method based on joint sparse representation [39]. is method explored the internal associations between different views through joint sparse representation, thereby improving the accuracy of sparse representation of each view
Based on the above analysis, this paper fully examines the inner correlation in the multiview SAR images for target recognition
Summary
With the continuous enhancement of synthetic aperture radar (SAR) data acquisition capabilities, it has become possible to acquire SAR images of the same target from multiple views, which provides more available information for correct target recognition [1]. In [33], researchers made use of SAR images from multiple views based on Bayesian theory-fused decision-making to obtain more reliable recognition results. Zhang et al first proposed a multiview SAR target recognition method based on joint sparse representation [39]. Erefore, both independent inspection and joint representation are too arbitrary and cannot fully reflect the information contained in multiview SAR images. Based on the above analysis, this paper fully examines the inner correlation in the multiview SAR images for target recognition. Erefore, several multiview subsets with strong correlations can be obtained according to the resulted entropies. As a multitask processing algorithm, the joint sparse representation [39,40,41] is used for each set of SAR images to investigate the internal correlations of different views. The MSTAR dataset is used to comprehensively evaluate the proposed method. e result shows the effectiveness of the method in this paper
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