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• W2172078172 abstract "A method for target classification in synthetic aperture radar (SAR) images is proposed. The samples are first mapped into a high-dimensional feature space in which samples from the same class are assumed to span a linear subspace. Then, any new sample can be uniquely represented by the training samples within given constraint. The conventional methods suggest searching the sparest representations with ℓ1-norm (or ℓ0) minimization constraint. However, these methods are computationally expensive due to optimizing nondifferential objective function. To improve the performance while reducing the computational consumption, a simple yet effective classification scheme called kernel linear representation (KLR) is presented. Different from the previous works, KLR limits the feasible set of representations with a much weaker constraint, ℓ2-norm minimization. Since, KLR can be solved in closed form there is no need to perform the ℓ1-minimization, and hence the calculation burden has been lessened. Meanwhile, the classification accuracy has been improved due to the relaxation of the constraint. Extensive experiments on a real SAR dataset demonstrate that the proposed method outperforms the kernel sparse models as well as the previous works performed on SAR target recognition." @default.
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• W2172078172 date "2014-06-16" @default.
• W2172078172 modified "2023-10-16" @default.
• W2172078172 title "Kernel linear representation: application to target recognition in synthetic aperture radar images" @default.
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• W2172078172 doi "https://doi.org/10.1117/1.jrs.8.083613" @default.
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