Measurement Image Translation-Automatic Target Recognition Technique Based on CycleGAN with SAR Simulation DB

• Published in: Journal of Electromagnetic Engineering and Science Vol. 22; no. 3; pp. 326 - 334
• Main Authors: Seo, Seung Mo, Choi, Yeoreum, Lim, Ho, Chae, Dae-Young
• Format: Journal Article
• Language: English
• Published: 한국전자파학회JEES 01.05.2022; The Korean Institute of Electromagnetic Engineering and Science; 한국전자파학회
• Subjects: automatic target recognition (atr); cycle generative adversarial network (cyclegan); deep learning (dl); inverse synthetic aperture radar (isar); machine learning (ml); synthetic aperture radar (sar); 전자/정보통신공학; Automatic Target Recognition (ATR); Machine Learning (ML); Cycle Generative Adversarial Network (CycleGAN); Inverse Synthetic Aperture Radar (ISAR); Deep Learning (DL); Synthetic Aperture Radar (SAR)
• ISSN: 2671-7255; 2671-7263
• DOI: 10.26866/jees.2022.3.r.94

The proposed approach achieves the reliable accuracy of synthetic aperture radar-automatic target recognition (SAR-ATR) with a simulation database. The simulation images of targets-of-interest are generated from inverse SAR using high-frequency techniques. A measurement image translation-automatic target recognition (MIT-ATR) uses two deep learning networks. The unique feature of the MIT-ATR is that the measurement images are translated to the simulation-like images by cycle generative adversarial network (CycleGAN). CycleGAN does not need to have a dataset of paired images between the measurement and simulation images. The generated simulation-like images are used as the inputs of the Visual Geometry Group (VGG) network. The VGG network is trained on a simulation database with a softmax layer of multi-classes. Five classes, including a T-72 tank, are considered in the numerical experiments. The images of each class are simulated at all azimuth angles, but the elevation angles range from 6° to 30°. The accuracy of the proposed approach is 63% better than that of the traditional method with only the VGG network. The simulation database could definitely supplement the lack of measurement data. The accuracy of MIT-ATR is properly handled by CycleGAN and the VGG network.