[1]崔颖,王雪婷,陆忠军,等.改进M-training算法的高光谱图像分类[J].哈尔滨工程大学学报,2018,39(10):1688-1694.[doi:10.11990/jheu.201707022]
 CUI Ying,WANG Xueting,LU Zhongjun,et al.Hyperspectral image classification based on improved M-training algorithm[J].hebgcdxxb,2018,39(10):1688-1694.[doi:10.11990/jheu.201707022]
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
39
期数:
2018年10期
页码:
1688-1694
栏目:
出版日期:
2018-10-05

文章信息/Info

Title:
Hyperspectral image classification based on improved M-training algorithm
作者:
崔颖12 王雪婷1 陆忠军2 王立国1
1. 哈尔滨工程大学 信息与通信工程学院, 黑龙江 哈尔滨 150001;
2. 黑龙江省农业科学院 遥感技术中心, 黑龙江 哈尔滨 150086
Author(s):
CUI Ying12 WANG Xueting1 LU Zhongjun2 WANG Liguo1
1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2. Remote Sensing Technology Center, Heilongjiang Academy of Agricultural Science, Harbin 150086, China
关键词:
高光谱图像半监督分类M-training算法错误率加权图像处理SVM分类器RF分类器KNN分类器
分类号:
TP75
DOI:
10.11990/jheu.201707022
文献标志码:
A
摘要:
为了解决高光谱数据有标签样本数量有限的分类问题,提出将M-training算法应用于高光谱图像分类。采用两个SVM、一个K近邻(KNN)以及一个随机森林(RF)进行分类器组合,对传统M-training算法进行改进,增强分类器的多样性和差异性。为了充分考虑大量无标签样本的影响,采用有标签样本与无标签样本错误率加权作为有标签样本集更新的限制条件,从而有效地扩大了有标签样本集。实验结果表明:改进算法和传统的M-training算法相比较,在总体分类精度与Kappa系数上分别提高1.85%~12.10%与0.021 5~0.141 3,从而验证了该算法的有效性。

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备注/Memo

备注/Memo:
收稿日期:2017-07-05。
基金项目:国家自然科学基金项目(61675051);教育部博士点基金项目(20132304110007);中央高校基本科研业务费专项资金号(HEUCFG201831).
作者简介:崔颖(1979-),女,副教授.
通讯作者:崔颖,E-mail:cuiying@hrbeu.edu.cn.
更新日期/Last Update: 2018-10-10