摘要
数据分类是将遥感数据转化为专题图信息的一个关键步骤.找到一个好的分类方法,提高数据处理的准确性是高度挑战性问题.提出一种极限学习机和支持向量机相融合的遥感图像分类模式,选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,而且消除了一些训练样本标签对分类的负面影响.结合PSM图像,与SVM、ANN(Artificial Neural Network)方法进行对比分析,表明该方法的鲁棒性.
Data classification is a key step of the remote sensing data into thematic map information. Find a good classification method and improve the accuracy of data processing are highly challenging problems. In this pa- per, we propose a fusion classification model based on Extreme Learning Machine (ELM) and Support Vector Ma- chine (SVM) for remote sensing image classification. Choose ELM based classifier and correction to improve the classification efficiency by SVM. Simulation experiment results show that the algorithm not only has higher classifi- cation accuracy, and eliminate some of the training sample tag on the negative impact for classification. Combining PSM images, comparative analysis with SVM and Artificial Neural Network (ANN), show the robustness of the meth- od.
出处
《伊犁师范学院学报(自然科学版)》
2017年第2期61-67,共7页
Journal of Yili Normal University:Natural Science Edition
基金
伊犁师范学院院级重点项目(2015YSZD04)
国家自然科学基金项目(61663045)
新疆高校科研计划重点研究项目(XJEDU2014I043)
吉林省科技发展计划项目(20120302)
关键词
支持向量机
极限学习机
遥感数据
分类精度
Support vector machines
Extreme learning machine
Remote sensing data
Classification accuracy