引用本文: | 陈永生,喻玲娟,谢晓春. 基于全卷积神经网络的SAR图像目标分类[J]. 雷达科学与技术, 2018, 16(3): 242-248.[点击复制] |
CHEN Yongsheng, YU Lingjuan, XIE Xiaochun. SAR Image Target Classification Based on All Convolutional Neural Network[J]. Radar Science and Technology, 2018, 16(3): 242-248.[点击复制] |
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摘要: |
近年来,卷积神经网络(Convolutional Neural Network,CNN)在合成孔径雷达(Synthetic Aperture Radar,SAR)图像目标分类中取得了较好的分类结果。CNN结构中,前面若干层由交替的卷积层、池化层堆叠而成,后面若干层为全连接层。全卷积神经网络(All Convolutional Neural Network, A-CNN)是对CNN结构的一种改进,其中池化层和全连接层都用卷积层代替,该结构已在计算机视觉领域被应用。针对公布的MSTAR数据集,提出了基于A-CNN的SAR图像目标分类方法,并与基于CNN的SAR图像分类方法进行对比。实验结果表明,基于A-CNN的SAR图像目标分类正确率要高于基于CNN的分类正确率。 |
关键词: 卷积神经网络 全卷积神经网络 合成孔径雷达 目标分类 |
DOI:10.3969/j.issn.1672-2337.2018.03.002 |
分类号:TN958;TN957.5 |
基金项目:国家自然科学基金(No.61501210);江西省自然科学基金(No.20161BAB202054);江西省教育厅科技项目(No.GJJ150684) |
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SAR Image Target Classification Based on All Convolutional Neural Network |
CHEN Yongsheng, YU Lingjuan, XIE Xiaochun
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1.School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China;2.School of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, China
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Abstract: |
Convolutional neural network (CNN) has achieved good performance in the target classification of synthetic aperture radar (SAR) images. In the CNN structure, the preceding layers are stacked by alternating convolutional and pooling layers, while the latter layers are the fully-connected layers. The all convolutional neural network (A-CNN) is an improvement on the CNN structure, where the pooling and fully-connected layers are replaced by convolutional layers. It has been applied in the field of computer vision. In this paper, a method of SAR image target classification based on A-CNN is proposed. A comparison of the proposed method with CNN-based method by use of the published MSTAR data set. The experimental results show that the accuracy of SAR image classification based on A-CNN is higher than that of CNN. |
Key words: convolutional neural network (CNN) all convolutional neural network synthetic aperture radar(SAR) target classification |