Abstract
Synthetic aperture radar (SAR) target recognition under extended operating conditions (EOCs) is a challenging problem due to the complex application environment, especially for insufficient target variations and corrupted SAR images in the training samples. This paper proposes a new strategy to solve these problems for target recognition. The SAR images are firstly characterized by multi-scale components of monogenic signal. The generated monogenic features are decomposed to learn a class dictionary and a shared dictionary, which represent the possible intraclass variations information and the common information, respectively. Moreover, a sparse representation of the class dictionary and a dense representation of the shared dictionary are jointly employed to represent a query sample for classification. The validity of the proposed strategy is demonstrated with multiple comparative experiments on moving and stationary target acquisition and recognition (MSTAR) database.
Highlights
Synthetic aperture radar (SAR) is an active sensor, which has the ability to provide full-time, full-weather, and high-resolution imagery [1]
Different from directly using the monogenic features to generate low-rank dictionary for spare representation, a class dictionary and a shared dictionary are learned through decomposing the monogenic features
When the percentage of occlusion increases, the recognition rates of LRSDL and FDDL quickly drop, while the performance of JMSDR is relatively stable. This can be attributed to the dense representation based on the shared dictionary, as the query sample is represented by more samples which have common information
Summary
Synthetic aperture radar (SAR) is an active sensor, which has the ability to provide full-time, full-weather, and high-resolution imagery [1]. Dong et al introduced the monogenic components to describe SAR images for target recognition by feeding them into the framework of sparse representation modeling [14]. Zhou et al [17] presented the feature fusion of multi-scale monogenic components by 2D canonical correlation analysis for SAR target recognition. These studies prove the ability of the sparse representation of monogenic components in SAR target recognition. These methods directly produce the dictionary with the training samples in sparse representation modeling; the correlated versions of atoms in dictionary will limit the target reconstruction and discrimination
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