期刊
ELECTRONICS
卷 9, 期 4, 页码 -出版社
MDPI
DOI: 10.3390/electronics9040555
关键词
circular SAR; vehicle target recognition; contour thinning; residual compensation; convolutional neural network
资金
- National Natural Science Foundation of China [61775030, 61571096]
- Sichuan Science and Technology Program [2019YJ0167]
- Open Research Fund of Key Laboratory of Optical Engineering, Chinese Academy of Sciences [2017LBC003]
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.
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