[1]盛振国,王立国.改进的LLGC高光谱图像半监督分类[J].哈尔滨工程大学学报,2017,38(07):1086-1092.[doi:10.11990/jheu.201605023]
 SHENG Zhenguo,WANG Liguo.Semi-supervised classification for hyperspectral images based on improved learning with the LLGC method[J].hebgcdxxb,2017,38(07):1086-1092.[doi:10.11990/jheu.201605023]
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改进的LLGC高光谱图像半监督分类(/HTML)
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
38
期数:
2017年07期
页码:
1086-1092
栏目:
出版日期:
2017-07-25

文章信息/Info

Title:
Semi-supervised classification for hyperspectral images based on improved learning with the LLGC method
作者:
盛振国12 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. 江南机电设计研究所, 贵州 贵阳 550009
Author(s):
SHENG Zhenguo12 WANG Liguo1
1. College of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, China;
2. Jiangnan Design Institute of Machine and Electricity, Guiyang 550099, China
关键词:
半监督分类局部全局一致性边缘采样法KNN算法高光谱图像无标鉴样本集
分类号:
TN911.73
DOI:
10.11990/jheu.201605023
文献标志码:
A
摘要:
针对高光谱数据波段多,地物标签获取代价高,带标记的样本数量少,分类过程中容易引起Hudges现象。本文提出一种基于改进的局部全局一致性(learning with local and global consistency,LLGC)算法的半监督分类方法。通过边缘采样法(margin sampling,MS)选取最富含信息量的无标签样本,加入到训练集来扩充训练样本;用KNN算法计算相似度进一步优选无标签样本,去除噪声点和存在的野值点;使用改进的局部全局一致性算法对无标签样本集进行分类标记,得到各类别的分类结果。实验结果表明,本文方法在充分利用无标签样本的情况下,有效地提高了带有少量标签样本的高光谱图像的分类精度。

参考文献/References:

[1] 童庆禧,张兵,郑兰芬. 高光谱遥感[M]. 北京:高等教育出版社,2006.
[2] SHAHSHAHANI B M,LANDGREBE D A. The effect of unlabeled samples in reducing the small sample size problem and mitigating the hughes phenomenon[J].IEEE Trans. Geosci. Remote Sens, 1994,17(9):1087-1095.
[3] ZHANG D,ZHOU Z,CHEN S. Semi-supervised dimensionality reduction[C]//Proceedings of the 7th International Conference on Data Mining.Omaha,USA, 2007:629-634.
[4] CHAPELLE O, SCHOLKOPF B. Semisupervised learning[M].Cambridge:MIT Press, 2006.
[5] NIGAM K, CHANI R. Analyzing the effectiveness and applicability of co-training[C]//Proceedings of the Ninth International Conference on Information and Knowledge Management.Halifax, 2000:86-93.
[6] BLUM A, MITCHELL T. Combining labeled and unlabeled data with co-training[C]//Proceedings of the Eleventh Annual Conference on Computational Learning Theory.New York, USA, 1998:92-100.
[7] MILLER D, UYAR H S. A mixture of experts classifier with learning based on both labelled and unlabelled data[C]//Advances in Neural Information Processing Systems. Cambridge, MA, USA,1997:571-577.
[8] NIGAM K,MCCALLUM, THRUN S.Text classification from labeled and unlabeled documents using EM[J]. Machine learning, 2000, 39(3):103-134.
[9] JOACHIMS T. Transductive inference for text classification usingsupport vector machines[C]//Proceedings of the Sixteenth International Conference on Machine Learning. San Francisco, CA, USA 1999:200-209.
[10] BELKIN M, NIYOGI P, SINDHWANI. Manifold regularization:A geometric framework for learning from labeled and unlabeled examples[J]. The journal of machine learning research, 2006, 7(11):2399-2434.
[11] ZHOU Z H,LI M. Tri-training:exploiting unlabeled data using three classifiers[J].IEEE transactions on knowledge and data engineering, 2005, 17(11):1529-1541.
[12] 王立国, 张晔, 谷延锋.支持向量机多类目标分类器的结构简化研究[J]. 中国图象图形学报, 2005, 10(5):571-572.WANG Liguo,ZHANG Ye,GU Yanfeng. The research of simplification of structure of multi-class classifier of support vector mach ine[J]. Journal of image and graphics, 2005,10(5):571-572.
[13] MARCONCINI M,CAMPLES G, BRUZZONE L.A composite semi-supervised SVM for classification of hyperspectral imaages[J]. IEEE geoscinence and remote sensing letters, 2009, 6(2):234-238.
[14] JOACHIMS T. Transductive inference for text classification using support vector machines[C]//Proceedings of the Twenty-first International Conference on Machine Learning. San Francisco, CA, USA, 1999:200-209.
[15] BLUM A, CHAWLA S. Learning from labeled and unlabeled data using graph mincuts[C]//Proceedings of the 18th international conference on machine learning. Williamstwn MA, USA, 2001:19-26.
[16] ZHOU D Y, BOUSQUET O,LAL T N,et al.Learning with local and global consistency[C]//Proceedings of Advances in Neural Information Processing Systems.Tuebingen,Germany, 2004:321-328.
[17] BAI Bendu, FAN Jiulun. Learning with local and global consistency based on sparse representation[J]. Journal of Xi’an university of posts and telecommunications, 2003, 7(4):79-85.
[18] GUI Jie,HUANG Deshuang,YOU Zhuhong.An improvement on learning with local and global consistency[C]//Proceedings of the 19th International Conference on Pattern Recognition.Tampa,FL, USA, 2008:1-4.
[19] NGUYEN H T,SMEULDERS A.Active learning using pre-clustering[C]//Proceedings of the Twenty-First International Conference on Machine Learning,Canada, 2004:79-80.
[20] SCHOHN G,COHN D. Less is more active learning with support vectors machines[C]//Proceedings of the Twenty-First International Conference on Machine Learning, Stanford, 2000:839-846.
[21] CAMPBELL C, CRISTIANINI N,SMOLA A. Query learning with large margin classifiers[C]//Proceedings of the Twenty-First International Conference on Machine Learning. Stanford, 2000:111-118.
[22] SEUNG H,OPPER M,SOMPLINSKY H. Query by committee[C]//Proceedings of the Twenty-First International Conference on Machine Learning. Stanford, 2000:111-118.
[23] FREUND Y, SEUNG H, SHAMIR E. Selective sampling using the query by committee algorithm[J]. Machine learning, 1997, 28(8):133-168.

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备注/Memo

备注/Memo:
收稿日期:2016-05-07。
基金项目:国家自然科学基金项目(60802059);教育部博士点新教师基金项目(200802171003).
作者简介:盛振国(1992-),男,硕士;王立国(1974-),男,教授,博士生导师.
通讯作者:王立国,E-mail:wangliguo@hrbeu.edu.cn.
更新日期/Last Update: 2017-08-28