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An Object Recognition Approach for Synthetic Aperture Radar Images


In this paper, an object recognition approach for synthetic aperture radar (SAR) images is addressed, which is based on the enhanced kernel sparse representation of monogenic signal. It consists of two main modules. In the first module, to capture the spatial and spectral properties of a target at the same time, a multi-scale monogenic feature extraction scheme is proposed. In the second module, an enhanced kernel sparse representation-based classifier (KSRC) is designed. Different from the traditional KSRC, in the enhanced KSRC, we first integrate the kernel principal component analysis (KPCA) as well as the kernel fisher discriminant analysis (KFDA) to generate an augmented pseudo-transformation matrix. Then, a new discriminative feature mapping approach is presented by exploiting the augmented pseudo-transformation matrix so that the dimensionality of the kernel feature space can be effectively reduced. At last, the 1 -norm minimization is utilized to calculate the sparse coefficients for a test sample, and thus the inference can be reached in terms of the total reconstruction error. Experimental results on the public moving and stationary target acquisition and recognition dataset (MSTAR) demonstrate that the proposed method achieves high recognition accuracy for SAR automatic target recognition.

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This work was supported by the National Natural Science Foundation of China (Grant No. 61603124, 61871218, 61801211, 61501233), Funding of Jiangsu Innovation Program for Graduate Education (Grant No. KYLX15_0278), Fundamental Research Funds for the Central Universities (Grant No. 2019B15314, 3082017NP2017421), and the Aeronautical Science Foundation of China (Grant No. 20152052026).

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Correspondence to Wenbo Liu.

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Ning, C., Liu, W., Zhang, G. et al. An Object Recognition Approach for Synthetic Aperture Radar Images. Mobile Netw Appl 26, 1259–1266 (2021).

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  • Advanced image processing
  • Target recognition
  • Sparse representation
  • Monogenic signal