Abstract
For the synthetic aperture radar (SAR) target recognition problem, a method combining multifeature joint classification and adaptive weighting is proposed with innovations in fusion strategies. Zernike moments, nonnegative matrix factorization (NMF), and monogenic signal are employed as the feature extraction algorithms to describe the characteristics of original SAR images with three corresponding feature vectors. Based on the joint sparse representation model, the three types of features are jointly represented. For the reconstruction error vectors from different features, an adaptive weighting algorithm is used for decision fusion. That is, the weights are adaptively obtained under the framework of linear fusion to achieve a good fusion result. Finally, the target label is determined according to the fused error vector. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset under the standard operating condition (SOC) and four extended operating conditions (EOC), i.e., configuration variants, depression angle variances, noise interference, and partial occlusion. The results verify the effectiveness and robustness of the proposed method.
Highlights
Synthetic aperture radar (SAR) target recognition has been researched for decades since 1990s [1]
The features used in SAR target recognition cover geometric ones, transformation ones, and electromagnetic ones. e geometric shape features describe the target area and contour distributions [11,12,13,14,15,16,17,18,19,20,21], such as the Zernike moments, outline descriptors. e transformation features can be further divided into two sub-categorifies as projection and decomposition ones. e former aims to find the optimal projection directions through the learning of training samples, so the high dimension of the original images can be reduced efficiently
Typical algorithms for projection features include principal component analysis (PCA) [22], nonnegative matrix factorization (NFM) [23], etc. e latter decomposes the original image through a series of signal bases to obtain different layers of descriptors. e representation algorithms for decomposition features include wavelet decomposition [24], monogenic signal [25, 26], bidimensional empirical mode decomposition (BEMD) [27], etc. e electromagnetic features focus on radar backscattering characteristics of targets, e.g., the attributed scattering center [28,29,30,31,32]
Summary
Synthetic aperture radar (SAR) target recognition has been researched for decades since 1990s [1]. From the aspect of target descriptions, the methods can be categorized as template-based and model-based ones In the former, the references for the test sample are described by SAR images from different conditions, e.g., azimuths, depression angles, backgrounds, called training samples [2,3,4]. Ree types of features, i.e., Zernike moments, NMF, and monogenic signal, are used to describe the target characteristics in SAR images, which reflect the target shape, pixel distribution, time-frequency properties, respectively. Based on the previous works, this paper proposes a SAR target recognition method via a combination of joint representation of multiple features and adaptive weighting. Tests and verifications are carried out on the moving and stationary target acquisition and recognition (MSTAR) dataset. e results of typical experimental setups show the effectiveness and robustness of the proposed method
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