基于极限学习机的GF-2影像分类

doi:10.13278/j.cnki.jjuese.20170271

吉林大学学报(地球科学版) ›› 2018, Vol. 48 ›› Issue (2): 373-378.doi: 10.13278/j.cnki.jjuese.20170271

• 地球物理数据处理与解释技术 • 上一篇下一篇

基于极限学习机的GF-2影像分类

王明常^1,2,3, 张馨月¹, 张旭晴¹, 王凤艳¹, 牛雪峰¹, 王红²

1. 吉林大学地球探测科学与技术学院, 长春 130026;
2. 湖北大学资源环境学院, 武汉 430062;
3. 国土资源部城市土地资源监测与仿真重点实验室, 广东深圳 518000

收稿日期:2017-09-07 出版日期:2018-03-26 发布日期:2018-03-26
通讯作者: 王红(1975-),女,副教授,博士,主要从事遥感与地理信息系统方面的教学研究,E-mail:j-wanghong@163.com E-mail:j-wanghong@163.com
作者简介:王明常(1975-),男,教授,博士,主要从事遥感与地理信息系统方面的教学研究,E-mail:wangmc@jlu.edu.cn
基金资助:
国家自然科学基金项目（41430322）；国土资源部城市土地资源监测与仿真重点实验室开放基金项目（KF-2016-02-008）；区域开发与环境响应湖北省重点实验室开放研究基金项目（2015（B）003）

GF-2 Image Classification Based on Extreme Learning Machine

Wang Mingchang^1,2,3, Zhang Xinyue¹, Zhang Xuqing¹, Wang Fengyan¹, Niu Xuefeng¹, Wang Hong²

1. College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China;
2. Faculty of Resources and Environment, Hubei University, Wuhan 430062, China;
3. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518000, Guangdong, China

Received:2017-09-07 Online:2018-03-26 Published:2018-03-26
Supported by:
Supported by National Natural Science Foundation of China(41430322), Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources (KF-2016-02-008) and Open Research Fund Program of Hubei Province Key Laboratory of Regional Development and Environmental Response(2015(B)003)

摘要/Abstract

摘要： 遥感图像分类是提取图像有效信息过程中重要的一部分，为了探寻最优的分类方法，许多机器学习算法逐步应用于遥感分类中。极限学习机（extreme learning machine，ELM）以其高效、快速和良好的泛化性能在模式识别领域得到广泛应用。本文采用训练速度快、运算量小的极限学习机算法与支持向量机（support vector machines，SVM）算法和最大似然法进行分类对比，对高分辨率遥感图像进行分类，分析极限学习机算法对于遥感图像分类的准确度等性能。选取吉林省长春市部分区域的GF-2遥感数据，将融合后的影像设置为原始数据，利用3种方法进行分类。研究结果表明，极限学习机算法分类图像总体分类精度达到85%以上，kappa系数达到0.718，与其他分类方法相比分类准确度较高，且极限学习机运行时间比支持向量机运行时间约短2 480 s，约为支持向量机运行时间的1/8，因此具有良好的性能和实用价值。

关键词: 极限学习机, 遥感图像分类, GF-2影像, 监督分类, 支持向量机

Abstract: The classification of remote sensing image is an important part of extracting effective information of images. In order to explore the optimal classification methods, many machine learning algorithms are gradually applied to the classification of remote sensing images. Because of the high efficiency, speediness, and good performance generalization, extreme learning machines (ELM) have been widely used in pattern recognition. This paper aims to classify high-resolution remote sensing images, and analyze the performance of extreme learning machine algorithms for the accuracy of classification of remote sensing images, and compare ELM algorithm with support vector machines (SVM) algorithm and Maximum likelihood method. The GF-2 data from some area in Changchun City were selected to test the accuracy of all the three methods for classification with the fused image as the original data. The results show that the overall accuracy of ELM algorithm is more than 85%, and the kappa coefficient is 0.718. Compared with the other two classification methods, the ELM classification accuracy is the best, and its running time is faster than the support vector machine by 2 480 s, 1/8 of the support vector machine's. An even better performance can be obtained in a shorter training time. ELM is valuable for classification of remote sensing images.

Key words: extreme learning machine, remote sensing image classification, GF-2 image, supervised classification, support vector machines

中图分类号:

P237

王明常, 张馨月, 张旭晴, 王凤艳, 牛雪峰, 王红. 基于极限学习机的GF-2影像分类[J]. 吉林大学学报(地球科学版), 2018, 48(2): 373-378.

Wang Mingchang, Zhang Xinyue, Zhang Xuqing, Wang Fengyan, Niu Xuefeng, Wang Hong. GF-2 Image Classification Based on Extreme Learning Machine[J]. Journal of Jilin University(Earth Science Edition), 2018, 48(2): 373-378.

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基于极限学习机的GF-2影像分类

GF-2 Image Classification Based on Extreme Learning Machine

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