Automatic Target Recognition of Military SAR Images Using Super-Resolution based Convolutional Neural Network

被引：0

作者：

Ryu, Jemin ^{[1
]}

Ma, Jungmok ^{[1
]}

机构：

[1] Department of Defense Science, Korea National Defense University, Korea, Republic of

来源：

Journal of Institute of Control, Robotics and Systems | 2022年 / 28卷 / 01期

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摘要：

Herein, we propose employing the super-resolution-based convolutional neural network (CNN) to design the automatic target recognition (ATR) of military synthetic aperture radar (SAR) images. Previous SAR ATR methods showed a good recognition performance with a low depression angle, but poor performance with a high depression angle. In a warfighting environment, good recognition performance is required even with a high depression angle. To address this issue, we combine the super-resolution method and the CNN. In comparison with the conventional VGGnet with a high depression angle, the proposed super-resolution-based CNN showed a 3%-4% improvement in accuracy. The MSTAR SAR dataset was utilized for validation. © ICROS 2022.

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页码：22 / 27