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Open AccessArticle

CNN-Based Vehicle Target Recognition with Residual Compensation for Circular SAR Imaging

by Rongchun Hu 1,2,3, Zhenming Peng 1,2,*, Juan Ma 4 and Wei Li 1,2
1
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2
Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu 610054, China
3
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China
4
School of Science, Southwest University of Science and Technology, Mianyang 621010, China
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(4), 555; https://doi.org/10.3390/electronics9040555
Received: 11 February 2020 / Revised: 13 March 2020 / Accepted: 24 March 2020 / Published: 26 March 2020
(This article belongs to the Section Microwave and Wireless Communications)
The contour thinning algorithm is an imaging algorithm for circular synthetic aperture radar (SAR) that can obtain clear target contours and has been successfully used for circular SAR (CSAR) target recognition. However, the contour thinning imaging algorithm loses some details when thinning the contour, which needs to be improved. This paper presents an improved contour thinning imaging algorithm based on residual compensation. In this algorithm, the residual image is obtained by subtracting the contour thinning image from the traditional backprojection image. Then, the compensation information is extracted from the residual image by repeatedly using the gravitation-based speckle reduction algorithm. Finally, the extracted compensation image is superimposed on the contour thinning image to obtain a compensated contour thinning image. The proposed algorithm is demonstrated on the Gotcha dataset. The convolutional neural network (CNN) is used to recognize the target image. The experimental results show that the image after compensation has a higher target recognition accuracy than the image before compensation. View Full-Text
Keywords: circular SAR; vehicle target recognition; contour thinning; residual compensation; convolutional neural network circular SAR; vehicle target recognition; contour thinning; residual compensation; convolutional neural network
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Hu, R.; Peng, Z.; Ma, J.; Li, W. CNN-Based Vehicle Target Recognition with Residual Compensation for Circular SAR Imaging. Electronics 2020, 9, 555.

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