一种基于ELM-SVM的遥感图像分类方法

被引：3

作者：

古丽娜孜艾力木江 ^{[1
,2
]}

乎西旦居马洪 ^{[1
]}

孙铁利 ^{[3
]}

梁义 ^{[1
]}

机构：

[1] 伊犁师范学院电子与信息工程学院

[2] 东北师范大学地理科学学院

[3] 东北师范大学计算机科学与信息技术学院

来源：

东北师大学报(自然科学版) | 2017年 / 49卷 / 01期

关键词：

支持向量机; 遥感数据; 极限学习机; 分类精度;

D O I：

10.16163/j.cnki.22-1123/n.2017.01.011

中图分类号：

TP751 [图像处理方法];

学科分类号：

081002 ;

摘要：

提出一种极限学习机(ELM)和支持向量机(SVM)相融合的遥感图像分类模式.选取ELM为基础分类器,以SVM来修正改善分类效率.仿真实验结果表明,该算法不仅具有较高的分类精度,而且消除一些训练样本标签对分类的负面影响.结合ALOS/PALSAR、PSM图像与SVM、ANN(Artificial Neural Network)方法进行对比分析,发现该方法鲁棒性较好.

引用

页码：53 / 61

页数：9

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