计算机应用研究2017，Vol.34Issue(11)：3518-3520,3.DOI:10.3969/j.issn.1001-3695.2017.11.071
基于卷积神经网络的军事图像分类
Military image classification based on convolutional neural network
摘要
Abstract
The classification accuracy of the traditional visual features can not meet the requirements in the application of modem military affairs due to the extremely high similarity of the different objects in the battlefield and low recognition rate in complex conditions.This paper presented a new architecture of the convolutional neural network (CNN) based on PCA whitening for solving the classification problem of large-scale images which contained some specific military objects.It efficiently eliminated the relativity of sample data,enhanced the learning ability and improved the accuracy of the object recognition.It tested and evaluated the new CNN model with the large-scale data from military images and compared with the traditional methods.The experiment results show that the algorithm has higher recognition rate on military object recognition.关键词
军事图像分类/深度学习/卷积神经网络/主成分分析白化/随机池化Key words
military image classification/deep learning/convolutional neural network/PCA whitening/stochastic-pooling分类
信息技术与安全科学引用本文复制引用
高惠琳..基于卷积神经网络的军事图像分类[J].计算机应用研究,2017,34(11):3518-3520,3.基金项目
国家自然科学基金创新研究群体资助项目(61321002) （61321002）
国家自然科学基金重大国际合作项目(61120106010) （61120106010）
国家教育部长江学者创新团队资助项目(IRT1208) （IRT1208）
