A novel synthetic aperture radar (SAR) automatic target recognition (ATR) approach based on Curvelet Transform is
proposed. However, the existing approaches can not extract the more effective feature. In this paper, our method is
concentrated on a new effective representation of the moving and stationary target acquisition and recognition (MSTAR)
database to obtain a more accurate target region and reduce feature dimension. Firstly, MSTAR database can be
extracted feature through the optimal sparse representation by curvelets to obtain a clear target region. However,
considering the loss of part of edges of image. We extract coarse feature, which is to compensate fine feature error
brought by segmentation. The final features consisting of fine and coarse feature are classified by SVM with Gaussian
radial basis function (RBF) kernel. The experiments show that our proposed algorithm can achieve a better correct
classification rate.
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