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16 June 2014 Kernel linear representation: application to target recognition in synthetic aperture radar images
Ganggang Dong, Na Wang, Gangyao Kuang, Yinfa Zhang
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Journal of Applied Remote Sensing, Vol. 8, Issue 1, 083613 (June 2014). https://doi.org/10.1117/1.JRS.8.083613
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 ℓ 0 ) 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, ℓ 2 -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.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Ganggang Dong, Na Wang, Gangyao Kuang, and Yinfa Zhang "Kernel linear representation: application to target recognition in synthetic aperture radar images," Journal of Applied Remote Sensing 8(1), 083613 (16 June 2014). https://doi.org/10.1117/1.JRS.8.083613
Published: 16 June 2014
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Cited by 20 scholarly publications.
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KEYWORDS
Target recognition
Synthetic aperture radar
Detection and tracking algorithms
Associative arrays
Automatic target recognition
Computer programming
Image classification
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