SAR Target Recognition Via Features Extracted From Monogenic Signal

This paper studies and summarizes a feature extraction strategy from the multiscale monogenic space for target recognition in synthetic aperture radar (SAR) images. We propose applying the regional Euclidean (L2) norm to extract the features of the monogenic signal. It can obtain the maximum eigenvalue in each region and reduce information loss. We performed several groups of experiments on dimensionality reduction, they show that the regional L2 norm extraction method has the best classification results. MSTAR, OpenSARship, and FUSAR-ship datasets are the commonly used verification datasets for SAR target recognition. The experiments on these datasets have demonstrated the effectiveness of the monogenic signal extracted with regional L2 norm for SAR target recognition. In combination with K-Nearest Neighbor (KNN), sparse representation classifier (SRC), or support vector machines (SVM), the proposed method achieves great results compared with some advanced algorithms.