计算机应用与软件2024，Vol.41Issue(7)：159-164,183,7.DOI:10.3969/j.issn.1000-386x.2024.07.024
基于卷积神经网络的SAR图像舰船分类
SHIP CLASSIFICATION OF SAR IMAGE BASED ON CONVOLUTIONAL NEURAL NETWORKS
摘要
Abstract
In view of the problem that speckled noise in synthetic aperture radar(SAR)image leads to low accuracy of image classification,a classification algorithm based on improved VGG16 is proposed.A layer of attention was added to the convolution layer to focus on important features and suppress unimportant features,so as to suppress speckled noise.The Fisher loss function was introduced in the objective function,which was used to restrain the within-class and between-class distance of the feature,so as to reduce the classification errors caused by speckle noise.Through the experiments,it can be seen that the classification accuracy is improved by 5.63 percentage points,compared with the original network,which can effectively improve the problem of low classification accuracy caused by speckled noise.关键词
卷积神经网络/图像分类/注意力机制/Fisher线性判别准则/合成孔径雷达/斑点噪声Key words
Convolution neural network/Image classification/Attention mechanism/Fisher linear discrimination criterion/Synthetic aperture radar/Speckle noise分类
信息技术与安全科学引用本文复制引用
陈玮,刘坤..基于卷积神经网络的SAR图像舰船分类[J].计算机应用与软件,2024,41(7):159-164,183,7.基金项目
航空科学基金项目(201955015001). （201955015001）
