摘要
针对合成孔径雷达(SAR)图像目标识别中的分类决策问题,提出了一种基于更新分类器的新识别方法。该方法用卷积神经网络和稀疏表示分类器作为基础分类器对类别未知的样本进行分类,对两种方法的决策结果进行融合,并判定决策结果的可靠性。将类别可靠的测试样本补充到原始训练样本中以更新分类器,从而获得更可靠的识别结果。基于MSTAR数据集的实验结果表明,相比其他方法,本方法的识别准确率更高。
To address the classification decision problems in synthetic aperture radar(SAR)image target recognition,a new recognition method based on updated classifier is proposed in this paper.The method uses a convolutional neural network and a sparse representation classifier as the basic classifier to classify samples with unknown categories.The decision results of the two methods are fused,and the reliability of the fused decision results is then determined.Subsequently,test samples with reliable categories are added to the original training samples to update the classifier to obtain more reliable recognition results.The experimental results based on the MSTAR data set show that the recognition accuracy of the method is higher than those of the other methods.
作者
张振中
Zhang Zhenzhong(College of Equipment Management and Support,Engineering University of PAP,Xi'an,Shaanxi 710086,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第14期226-233,共8页
Laser & Optoelectronics Progress
基金
广东省科学技术委员会基金(cstc2015jcsfA90002)。
关键词
图像处理
合成孔径雷达
目标识别
卷积神经网络
稀疏表示分类器
更新分类器
image processing
synthetic aperture radar
target recognition
convolutional neural network
sparse representation classifier
updated classifier
