A PERFORMANCE ANALYSIS OF CONVOLUTIONAL NEURAL NETWORK MODELS IN SAR TARGET RECOGNITION

被引：0

作者：

Shao, Jiaqi ^{[1
]}

Qu, Changwen ^{[1
]}

Li, Jianwei ^{[1
]}

机构：

[1] Naval Aeronaut & Astronaut Univ, Dept Elect & Informat Engn, Erma Rd 188, Yantai 264001, Peoples R China

来源：

PROCEEDINGS OF 2017 SAR IN BIG DATA ERA: MODELS, METHODS AND APPLICATIONS (BIGSARDATA) | 2017年

关键词：

Deep learning; Convolutional neural networks; SAR target recognition;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In recent years, the deep learning method represented by Convolutional Neural Network (CNN) has made great progress in the field of image recognition. In this paper, the representative convolution neural network models such as AlexNet, VGGNet, GoogLeNet, ResNet, DenseNet, SENet and so on are applied to SAR image target recognition. According to the accuracy, parameter quantity, training time and other indicators, the performance of different CNN models are analyzed and compared on MSTAR data set, the superiority of CNN model in SAR image target recognition is verified, and it also provides a reference for the follow-up work in this field.

引用

页数：6

共 50 条

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