高技术通讯2017，Vol.27Issue(3)：203-212,10.DOI:10.3772/j.issn.1002-0470.2017.03.002
基于卷积神经网络模型的遥感图像分类
Remote sensing image classification based on CNN model
摘要
Abstract
The remote sensing image classification was studied.In consideration of the problems of feature extraction difficulty and low classification accuracy of the shallow structure classification model of support vector machine, a convolutional neural network model was designed for remote sensing image classification.The model comprises the input layer, convolution layer, full connection layer and output layer, and uses the SoftMax classifier for classification.The LandsatTM5 remote sensing image of Fujin city in June 6, 2010 was used as the data source to perform the classification experiment.The experimental results show that the proposed model employs several convolutional and pooling layers to extract the nonlinear and invariant features from the remote sensing image.These features are useful for image classification and target detection.The classification accuracy of the model was 92.57% when it was used in this image.Compared to the support vector machine classifier, the classification accuracy of this model was improved by 5%.Therefore, this model has a greater advantage in remote sensing image classification.关键词
卷积神经网络(CNN)/模型/支持向量机(SVM)/特征提取/遥感图像分类Key words
convolutional neural network (CNN)/model/support vector machine (SVM)/feature extraction/remote sensing image classification引用本文复制引用
付秀丽,黎玲萍,毛克彪,谭雪兰,李建军,孙旭,左志远..基于卷积神经网络模型的遥感图像分类[J].高技术通讯,2017,27(3):203-212,10.基金项目
国家自然科学基金(41571427),国家重点研发计划重点专项(2016YFC0500203),北京市属高校拔尖人才(CIT&TCD201504047)和北京市教委科研计划 (KM201410017008)资助项目. （41571427）
