This study proposes a synthetic aperture radar (SAR) target-recognition method based on the fused features from the multiresolution\nrepresentations by 2D canonical correlation analysis (2DCCA). The multiresolution representations were demonstrated\nto be more discriminative than the solely original image. So, the joint classification of the multiresolution representations is\nbeneficial to the enhancement of SAR target recognition performance. 2DCCA is capable of exploiting the inner correlations of\nthe multiresolution representations while significantly reducing the redundancy. Therefore, the fused features can effectively\nconvey the discrimination capability of the multiresolution representations while relieving the storage and computational burdens\ncaused by the original high dimension. In the classification stage, the sparse representation-based classification (SRC) is employed\nto classify the fused features. SRC is an effective and robust classifier, which has been extensively validated in the previous works.\nThe moving and stationary target acquisition and recognition (MSTAR) data set is employed to evaluate the proposed method.\nAccording to the experimental results, the proposed method could achieve a high recognition rate of 97.63% for the 10 classes of\ntargets under the standard operating condition (SOC). Under the extended operating conditions (EOC) like configuration\nvariance, depression angle variance, and the robustness of the proposed method are also quantitively validated. In comparison\nwith some other SAR target recognition methods, the superiority of the proposed method can be effectively demonstrated.
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