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- 저자 : 류제민(Jemin Ryu) (검색결과 2 건)
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군사용 SAR 이미지 초해상화를 위한 딥러닝 기반의 네트워크 구조에 관한 연구
류제민(Jemin Ryu),마정목(Jungmok Ma) (사)한국CDE학회 2021 한국CDE학회 논문집 Vol.26 No.2
The Republic of Korea military is using SAR(Synthetic Aperture Radar) geographic intelligence to deal with security threats. However, human experts have difficulty on analyzing acquired SAR images and identifying military targets due to low resolution. In this paper, we study the deep learning-based network architecture fit for the super-resolution of military SAR images. Previous military SAR image super-resolution studies mainly conducted on improving the results of super-resolution, but it was difficult to find studies on network architecture. The proposed neural network is a deep learning-based super-resolution networks. And it consists of input, learning, upsampling, and output layers with real military SAR images. We show and experiment with networks for super-resolution of military SAR images, while focusing on the input and upsampling layers. Experiment results show that we able to find a suitable architecture of input and upsampling layers is discussed.
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초해상화 기반 CNN을 이용한 군사용 SAR 자동표적인식 모델 연구
류제민(Jemin Ryu),마정목(Jungmok Ma) 제어로봇시스템학회 2022 제어·로봇·시스템학회 논문지 Vol.28 No.1
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.
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