Synthetic Aperture Radar Target Recognition Based on Multidimensional Sparse Model

This paper introduces a novel classification strategy based on multi-dimensional sparse model for target recognition in Synthetic Aperture Radar (SAR) image. This method constructs dictionaries for each dimensions of SAR image instead of a single dictionary in traditional sparse model. In dictionary learning stage, this method adopts a dictionary-by-dictionary updating strategy. Under the premise of keeping the other dictionaries unchanged, each dictionary is updated using a traditional dictionary learning method. The sparsity coefficients of test samples in various categories of dictionaries are first calculated separately, and the test samples are classified using the sparse representation minimum error criterion. The experimental results on the MSTAR dataset show that compared with the traditional sparse models, this method can improve the target recognition rate while reducing the volume of dictionary. Therefore, in the field of SAR target identification, multidimensional sparse models have better recognition capabilities than traditional sparse models.