[1]崔亚楠,吴建平,朱辰龙,等.基于迁移学习仿真 SAR 图像的目标识别研究[J].计算机技术与发展,2021,31(10):43-48.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 008]
　CUI Ya-nan,WU Jian-ping,ZHU Chen-long,et al.Research on Target Recognition Based on CNN Simulated SAR Image Transfer Learning[J].,2021,31(10):43-48.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 008]
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基于迁移学习仿真 SAR 图像的目标识别研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年10期
页码:
43-48
栏目:
图形与图像
出版日期:
2021-10-10

文章信息/Info

Title:
Research on Target Recognition Based on CNN Simulated SAR Image Transfer Learning
文章编号:
1673-629X(2021)10-0043-06
作者:
崔亚楠1 ; 吴建平1; 2; 3 ; 朱辰龙1 ; 闫相如1
1. 云南大学 信息学院,云南 昆明 650504;
2. 云南省电子计算中心,云南 昆明 650223;
3. 云南省高校数字媒体技术重点实验室,云南 昆明 650223
Author(s):
CUI Ya-nan1 ; WU Jian-ping1; 2; 3 ; ZHU Chen-long1 ; YAN Xiang-ru1
1. School of Information Science & Engineering,Yunnan University,Kunming 650504,China;
2. Yunnan Provincial Electronic Computing Center,Kunming 650223,China;
3. Digital Media Technology Key Laboratory of Universities and Colleges in Yunnan Province,Kunming 650223,China
关键词:
卷积神经网络; 迁移学习; 合成孔径雷达; 仿真 SAR 图像; Inception-ResNet-v2
Keywords:
convolutional neural network; transfer learning; synthetic aperture radar; simulated SAR image; Inception-ResNet-v2
分类号:
TP753
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 10. 008
摘要:
合成孔径雷达(SAR) 图像的目标识别研究在军事、国防等领域具有特殊的应用价值。 为了更高效、准确地识别SAR 图像中的目标物,实验利用卷积神经网络对 SAR 图像进行训练,以获取良好的识别模型。 但小样本集合的 SAR 图像存在识别效果差,易导致结果过拟合等问题。 为此,研究并提出了一种基于卷积神经网络仿真 SAR 图像迁移学习的目标识别方法。 通过选取数据量较大的仿真 SAR 图像数据集预训练 Inception-ResNet-v2 网络模型,得到相应的网络参数。 结合迁移学习的方法,将预训练模型得到的网络参数迁移到目标模型上作为目标模型的初始化参数,使用 SAR 图像对目标模型进行识别训练,并同步进行参数优化和迭代训练。 实验有效解决了 SAR 图像数据不足所产生的过拟合问题,并且模型识别的准确率得到提升。 通过 MSTAR 数据集验证了该算法的有效性,识别的准确率达到 99. 57% 。
Abstract:
The research of synthetic aperture radar image target recognition has special application value in military,national defense andother fields. In order to recognize targets in SAR images more efficiently and accurately,convolutional neural networks are used for SAR image training to obtain an excellent recognition model through experiments. However,SAR images with small sample sets have poor recognition effects,which may easily lead to problems such as over-fitting of results. To this end,a target recognition method based on convolutional neural network simulation SAR image transfer learning is researched and proposed. A simulation SAR image data set with a large amount of data is selected to pre-train the Inception-ResNet-v2 network model to obtain the corresponding network parameters.Combined with the transfer learning method, the network parameters obtained by the pre - training model are transferred to the target model as the initialization parameters of the target model,and the target model is identified and trained using SAR images,and parameter optimization and iterative training are performed simultaneously. The experiment effectively solves the problem of over fitting caused by insufficient SAR image data,and the accuracy of model recognition is improved. The effectiveness of the algorithm is verified through the MSTAR data set,and the recognition accuracy reaches 99. 57% .

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更新日期/Last Update: 2021-10-10