It is necessary to recognize the target in the situation of military battlefield monitoring and civilian real-time monitoring. Sparse representation-based SAR image target recognition method uses training samples or feature information to construct an overcomplete dictionary, which will inevitably affect the recognition speed. In this paper, a method based on monogenic signal and sparse representation is presented for SAR image target recognition. In this method, the extended maximum average correlation height filter is used to train the samples and generate the templates. The monogenic features of the templates are extracted to construct subdictionaries, and the subdictionaries are combined to construct a cascade dictionary. Sparse representation coefficients of the testing samples over the cascade dictionary are calculated by the orthogonal matching tracking algorithm, and recognition is realized according to the energy of the sparse coefficients and voting recognition. The experimental results suggest that the new approach has good results in terms of recognition accuracy and recognition time.

中文翻译：

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基于单信号和稀疏表示的SAR图像目标识别

必须在军事战场监视和平民实时监视的情况下识别目标。基于稀疏表示的SAR图像目标识别方法是使用训练样本或特征信息来构造一个不完整的字典，这必然会影响识别速度。提出了一种基于单信号和稀疏表示的SAR图像目标识别方法。在这种方法中，扩展的最大平均相关高度滤波器用于训练样本并生成模板。提取模板的单基因特征以构建子词典，并将子词典组合以构建级联词典。利用正交匹配跟踪算法计算级联字典上测试样本的稀疏表示系数，并根据稀疏系数的能量和投票识别实现识别。实验结果表明，该新方法在识别精度和识别时间方面均取得了良好的效果。