Selection of multi-view SAR images via nonlinear correlation information entropy with application to target classification

ABSTRACT

A synthetic aperture radar (SAR) target classification method is proposed by properly selecting multiple views via the nonlinear correlation information entropy (NCIE). The optimal subset of multi-view SAR images are selected, which are assumed to share stable inner correlations. The joint sparse representation is adopted as the basic classification scheme for the selected views to exploit their inner correlations. According to the total reconstruction error of the selected multi-view SAR images, the target label can be decided. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is employed to test the proposed method and the results validate the its superiority over some reference methods.

Additional information

Funding

This work was supported by High-resolution Earth Observation System Major Project under Grant No. GFZX0404130302.