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Kernel sparse coding method for automatic target recognition in infrared imagery using covariance descriptor
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DOI：10.1016/j.infrared.2016.04.020.png)
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Abstract
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Automatic target recognition in infrared imagery is a challenging problem. In this paper, a kernel sparse coding method for infrared target recognition using covariance descriptor is proposed. First, covariance descriptor combining gray intensity and gradient information of the infrared target is extracted as a feature representation. Then, due to the reason that covariance descriptor lies in non-Euclidean manifold, kernel sparse coding theory is used to solve this problem. We verify the efficacy of the proposed algorithm in terms of the confusion matrices on the real images consisting of seven categories of infrared vehicle targets. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Automatic target recognition
Kernel sparse coding
Covariance descriptor
Log-Euclidean metric
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