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Remote Sens. 2019, 11(2), 135; https://doi.org/10.3390/rs11020135

Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network

1
Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi’an 710071, China
2
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
3
School of Electronic Engineering, Xidian University, Xi’an 710071, China
*
Authors to whom correspondence should be addressed.
Received: 16 November 2018 / Revised: 28 December 2018 / Accepted: 7 January 2019 / Published: 11 January 2019
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
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Abstract

Aiming at the problem of the difficulty of high-resolution synthetic aperture radar (SAR) image acquisition and poor feature characterization ability of low-resolution SAR image, this paper proposes a method of an automatic target recognition method for SAR images based on a super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN). First, the threshold segmentation is utilized to eliminate the SAR image background clutter and speckle noise and accurately extract target area of interest. Second, the low-resolution SAR image is enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in the SAR image. Third, the automatic classification and recognition for SAR image is realized by using DCNN with good generalization performance. Finally, the open data set, moving and stationary target acquisition and recognition, is utilized and good recognition results are obtained under standard operating condition and extended operating conditions, which verify the effectiveness, robustness, and good generalization performance of the proposed method. View Full-Text
Keywords: synthetic aperture radar (SAR); automatic target recognition (ATR); image segmentation; super-resolution generative adversarial network (SRGAN); deep convolutional neural network (DCNN) synthetic aperture radar (SAR); automatic target recognition (ATR); image segmentation; super-resolution generative adversarial network (SRGAN); deep convolutional neural network (DCNN)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Shi, X.; Zhou, F.; Yang, S.; Zhang, Z.; Su, T. Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network. Remote Sens. 2019, 11, 135.

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