Abstract
We propose radar image classification via pseudo-Zernike moments based sparse representations. We exploit invariance properties of pseudo-Zernike moments to augment redundancy in the sparsity representative dictionary by introducing auxiliary atoms. We employ complex radar signatures. We prove the validity of our proposed methods on the publicly available MSTAR dataset.
Highlights
Synthetic aperture radar (SAR) can provide all-weather imagery with a very high resolution [1]
1) Fixed Auxiliary Atoms (AuxFix): In case the measurements are obtained at random aspect angles, we propose to constitute the auxiliary atoms as an overall average of the PZmoments based measurements of each class, i.e., Jk f (Ak) = akj j=1 where akj =∆ abs(Zgjk), for k = 1, · · ·, K
We presented sparse representations for radar image classification by using pseudo-Zernike moments
Summary
Synthetic aperture radar (SAR) can provide all-weather imagery with a very high resolution [1]. Template based classification [4] requires generation of a large number of templates for each target and matching the test image with those templates in an exhaustive search manner Coding is done through an 1norm minimisation problem and the classification is based on a least-squares metric w.r.t. the group of columns specific to a particular class object This is known as sparse representation based classifier (SRC). In [24], PZ-moments have been used for radar classification based on its microDoppler signatures, with an SVC In both these cases, the emphasis has been on feature extraction w.r.t. PZmoments and not on the choice of an optimal classifier.
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