Target recognition in SAR images based on variational mode decomposition
Guangyu ZHOU1, Bangquan LIU1, Dan ZHANG2
1. College of Digital Technology and Engineering, Ningbo University of Finance and Economics, Ningbo 315175, China 2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
In order to improve synthetic aperture Radar (SAR) target recognition performance, the authors propose a method based on variational mode decomposition (VMD). First, the bidimensional VMD (BVMD) is employed to decompose SAR images, thus obtaining multi-mode representations. Afterwards, the joint sparse representation is employed to represent the multiple modes. Finally, the target label is determined based on the minimum reconstruction error. The proposed method was tested on the MSTAR dataset. It could achieve a recognition rate of 99.24% on 10 classes of targets under the standard operating condition (SOC). In addition, its performance outperforms some other SAR target recognition methods under configuration variance, depression angle variance, and noise corruption. The results have confirmed the validity of the proposed method.
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Guangyu ZHOU, Bangquan LIU, Dan ZHANG. Target recognition in SAR images based on variational mode decomposition. Remote Sensing for Land & Resources, 2020, 32(2): 33-39.
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