Selection of monogenic components based on Spearman rank correlation with application to SAR target recognition

Feature extraction is one of the key factors in synthetic aperture radar (SAR) target recognition. This paper proposes a SAR target recognition method by jointly using multi-level monogenic components. The monogenic signal is employed to decompose the original SAR images to obtain monogenic spectral components at different levels. Afterwards, the Spearman rank correlation is used to evaluate the similarities between different monogenic components and the original SAR image. A threshold is set to select those components with higher similarities with the original image. Then, the joint sparse representation is employed to classify the selected monogenic components for the classification. The proposed method is tested on the MSTAR public dataset under the standard operating condition (SOC) and several extended operating conditions (EOC) for the recognition task of a few ground vehicles. According to the experimental results, the proposed method could achieve an average recognition of 98.52% on ten classes of targets under SOC. The average recognition rates at 30° and 45° depression angles reach 98.15% and 72.06%, respectively. In addition, the proposed method could achieve good robustness under noise corruption. In comparison, the proposed method achieves superiority over several existing SAR target recognition methods.