Abstract
ABSTRACT This letter combines the multi-mode representations extracted by bidimensional empirical mode decomposition (BEMD) and deep residual networks (ResNet) for synthetic aperture radar (SAR) target recognition. Bidimensional intrinsic mode functions (BIMFs) are generated by BEMD to describe the target characteristics in SAR images. A special ResNet is designed and trained for each layer of BIMFs. The decisions from different BIMFs are linearly fused using a random weight matrix. Typical test scenarios are designed on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset to examine the proposed method. The results validate the validity and robustness of the method.
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