摘要
对于遥感图像分类过程中的问题,提出遗传算法LVQ神经网络来实现遥感图像的分类。将LVQ神经网络结合遗传算法,使用遗传算法最优阈值与权值实现网络训练,使分类精度得到提高。之后融合相似灰度值创建分类图像特征矢量,使特征矢量在神经网络中输入实现训练。学习矢量量化神经算法对初值非常敏感,对遥感图像分类精度具有一定影响。最后,为了对性能进行测试,在实验过程中对比本文分类方法和SVM决策树分类方法,通过实验结果表示,文中提出的分类方法的遥感图像分类精度为95.82%,与其他分类方法相比,分类精度得到进一步提高。
In order to solve the problems in the process of remote sensing image classification,a genetic algorithm LVQ(learning vector quantization)neural network is proposed to realize remote sensing image classification.The LVQ neural network is combined with genetic algorithm,and the optimal threshold and weight of genetic algorithm are used to train the network,so that the classification accuracy is improved.Then,similar gray values are fused to create characteristic vector of classified images,which are input into the neural network for training.LVQ neural algorithm is very sensitive to initial values and has a certain impact on the classification accuracy of remote sensing images.Finally,in order to test the performance,the classification method proposed in this paper was compared with the SVM decision tree classification method in the experimental process.The experimental results show that the classification accuracy of remote sensing images with the proposed method is 95.82%,and has been further improved in comparison with other classification methods.
作者
邓凌云
Lingyun(Chongqing College of Humanities,Science and Technology,Chongqing 401524,China)
出处
《现代电子技术》
北大核心
2020年第1期40-43,共4页
Modern Electronics Technique
基金
重庆人文科技学院（16CRKXJ07）
关键词
遥感图像分类
遗传算法
LVQ神经网络
网络训练
性能测试
精度评估
remote sensing image classification
genetic algorithm
LVQ neural network
network training
performance testing
accuracy assessment
