基于改进R-FCN的SAR图像识别

doi:10.12305/j.issn.1001-506X.2022.04.17

Abstract

Abstract:

With remarkable achievements in target recognition, deep learning provides new ideas for improving the accuracy and speed of target recognition in synthetic aperture radar (SAR) images. In this paper, region-based fully convolutional networks (R-FCN) are applied to SAR image target recognition, and good results have been achieved. In order to solve the problem of small data set and high data similarity, the R-FCN model based on transfer learning is proposed for target recognition in SAR images. The faster region convolutional neural networks (Faster R-CNN) and the R-FCN models are trained and optimized, and the experimental results are compared with the improved R-FCN model based on transfer learning proposed in this paper. The results show that the proposed method has better recognition effects and faster recognition speed for SAR images.

Key words: machine vision, target recognition, synthetic aperture radar(SAR), fully convolutional network (FCN), migration study

CLC Number:

TP751.1

Xiaoling ZHOU, Zhaoxia ZHANG, Ya LU, Qian WANG, Kunkun WANG. SAR image recognition based on improved R-FCN[J]. Systems Engineering and Electronics, 2022, 44(4): 1202-1209.

Figures/Tables 8

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References 32

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类型	Faster R-CNN			R-FCN			改进的R-FCN
类型	识别率	漏检率	误检率	识别率	漏检率	误检率	识别率	漏检率	误检率
2s1	86.6	7.0	7.7	97.8	0.7	1.4	98.7	0	1.3
D7	95.6	1.3	3.0	97.6	0.3	2.0	98.3	0	1.6
T62	82.3	4.7	12.7	97.6	0	2.3	99.3	0	0.7
ZIL131	90.3	3.7	5.7	95.6	1.0	3.3	99.7	0	0.3
ZSU	92.3	2.3	5.3	97.8	0.7	1.4	99.7	0	0.3
平均数	89.2	3.8	6.9	97.4	0.5	2.1	99.1	0	0.9

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SAR image recognition based on improved R-FCN

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