SAR Target Recognition via Joint Classification of Monogenic Components with Discrimination Analysis
School of Automation and Electrical Engineering, Zhejiang University of Science and Technology
No.318 Liuhe Road, Hangzhou, Zhejiang 310023, China
This paper proposes a method using joint classification of monogenic components with discrimination analysis for target recognition in synthetic aperture radar (SAR) images. Three monogenic components, namely, phase, amplitude, and orientation, are extracted from the original image and classified by joint sparse representation for target recognition. Considering that the three components may have different discrimination capabilities for different operating conditions, the discrimination analysis is incorporated into the classification scheme. The components with low discriminability are not used in the joint classification. Afterwards, those discriminative components for a certain condition are classified to determine the target type. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) to evaluate the performance of the proposed method.
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