空天预警研究学报2024，Vol.38Issue(3)：162-166,172,6.DOI:10.3969/j.issn.2097-180X.2024.03.002
改进卷积神经网络的SAR图像识别方法
SAR image recognition method of improved convolutional neural network
罗曼 1李新1
作者信息
- 1. 湖北省电子信息产品质量监督检验院,武汉 430000
- 折叠
摘要
Abstract
SAR images exist speckle noise and distinction between different image categories is poor,which makes it difficult to extract representative features.In order to solve this problem,an improved convolutional neu-ral network for SAR image recognition method is proposed.Firstly,a multi-scale feature extraction module is de-signed to fully extract the hidden information of SAR images by using convolution layers of different scales.Then,by improving classical residual neural network residual block,a dense residual block structure is designed to provide rich detailed information for the back layer and ensure the expression ability of output features.Finally,verification is performed on the MSTAR dataset.The experimental results show that the recognition rate of the proposed model on the test set is 99.17%,which is better than other methods.After salt-pepper noise of different proportions is added to the test set,the proposed model still shows good robustness.关键词
卷积神经网络/SAR图像/多尺度特征提取模块/密集残差块/鲁棒性Key words
convolutional neural network/SAR image/multi-scale feature extraction module/dense residual block/robustness分类
信息技术与安全科学引用本文复制引用
罗曼,李新..改进卷积神经网络的SAR图像识别方法[J].空天预警研究学报,2024,38(3):162-166,172,6.
