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http://dx.doi.org/10.9766/KIMST.2022.25.3.219

SAR Recognition of Target Variants Using Channel Attention Network without Dimensionality Reduction  

Park, Ji-Hoon (Defense AI Technology Center, Agency for Defense Development)
Choi, Yeo-Reum (Defense AI Technology Center, Agency for Defense Development)
Chae, Dae-Young (Defense AI Technology Center, Agency for Defense Development)
Lim, Ho (Defense AI Technology Center, Agency for Defense Development)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.25, no.3, 2022 , pp. 219-230 More about this Journal
Abstract
In implementing a robust automatic target recognition(ATR) system with synthetic aperture radar(SAR) imagery, one of the most important issues is accurate classification of target variants, which are the same targets with different serial numbers, configurations and versions, etc. In this paper, a deep learning network with channel attention modules is proposed to cope with the recognition problem for target variants based on the previous research findings that the channel attention mechanism selectively emphasizes the useful features for target recognition. Different from other existing attention methods, this paper employs the channel attention modules without dimensionality reduction along the channel direction from which direct correspondence between feature map channels can be preserved and the features valuable for recognizing SAR target variants can be effectively derived. Experiments with the public benchmark dataset demonstrate that the proposed scheme is superior to the network with other existing channel attention modules.
Keywords
Synthetic Aperture Radar; Automatic Target Recognition; Channel Attention; Dimensionality Reduction; Deep Learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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