[1]王立国,杜心平.K均值聚类和孪生支持向量机相结合的高光谱图像半监督分类[J].应用科技,2017,44(03):12-18.[doi:10.11991/yykj.201606010]
 WANG Liguo,DU Xinping.Semi-supervised classification of hyperspectral images applying the combination of K-mean clustering and twin support vector machine[J].Applied science and technology,2017,44(03):12-18.[doi:10.11991/yykj.201606010]
点击复制

K均值聚类和孪生支持向量机相结合的高光谱图像半监督分类(/HTML)
分享到:

《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第44卷
期数:
2017年03期
页码:
12-18
栏目:
现代电子技术
出版日期:
2017-06-05

文章信息/Info

Title:
Semi-supervised classification of hyperspectral images applying the combination of K-mean clustering and twin support vector machine
作者:
王立国 杜心平
哈尔滨工程大学 信息与通信工程学院, 黑龙江 哈尔滨 150001
Author(s):
WANG Liguo DU Xinping
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
高光谱图像半监督分类机器学习孪生支持向量机K均值聚类算法样本缩减分类精度
Keywords:
hyper-spectral imagesemi-supervised classificationmachine learningtwin-SVMK-mean clustering algorithmsample reductionclassification accuracy
分类号:
TP391
DOI:
10.11991/yykj.201606010
文献标志码:
A
摘要:
为解决高光谱数据维度高、波段之间相关性强、获取大量监督信息费时费力的问题,对高光谱图像的分类进行研究。半监督分类方法是基于传统的机器学习的一种分类方法,它可以利用少量带标签的监督信息和大量无监督信息解决获取大量监督信息问题。将分类精度高、分类时间长的孪生支持向量机分类方法与迭代速度快、收敛速度快的的K均值聚类方法结合,可以在基本不改变分类精度的前提下,大幅度缩减孪生支持向量机分类的样本数量,从而降低分类时计算的复杂度,缩短计算时间,最终缩短整个分类过程所需要时间,提高分类效率。
Abstract:
In order to solve the problems such as high dimensionality of hyper-spectral data,strong correlation among bands and the difficulty in requiring a large amount of supervised information,the classification of hyper-spectral image was researched.Semi-supervised classification is a method based on traditional machine learning,which utilizes a small quantity of labeled supervised information and a large amount of non-supervised information.By combining the twin-SVM which has the advantages of high classification accuracy,short classification time with the K-mean clustering algorithm which has the advantages of rapid iteration and convergence,under the premise of basically not changing the classification precision,the quantity of samples used in the classification of twin-SVM can be reduced largely,so as to reduce the calculation complexity and time in classification,and finally shorten the total classification time and improve the classification efficiency.

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] 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.
[3] CHAPELLE O, SCHOLKOPF B. Semisupervised learning[M]. Cambridge:MIT Press, 2006.
[4] 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:A辑, 2005, 10(5):571-574.
[5] CRISTIANINI N, SHAWE-TAYLOR J.支持向量机导论[M]. 李国正, 王猛, 曾华军, 译. 北京:电子工业出版社, 2004:50-55.
[6] BAYRO-CORROCHANO E J, ARANA-DANIEL N. Clifford support vector machines for classification, regression, and recurrence[J]. IEEE trans on neural networks, 2010, 21(11):1731-1746.
[7] ERTEKIN S, BOTTOU L, GILES C L. Nonconvex online support vector machines[J]. IEEE transactions on pattern analysis and machine intelligence, 2011, 33(2):368-375.
[8] ZHANG, Yunsheng, ZHANG Yongfei. Adaptive resource allocation with svm-based multi-hop video packet delay bound violation modeling[J]. Chinese journal of electronics, 2011, 20(2):261-267.
[9] JAYADEVA, KHEMCHANDANI R, CHANDRA S. Twin support vector machines for pattern classification[J]. IEEE trans on pattern analysis and machine intelligence, 2007, 29(5):905-910.
[10] SHAO Y H, ZHANG C H, WANG X B, et al. Improvements on twin support vector machines[J]. IEEE trans neural netw, 2011, 22(6):962-968.
[11] QI Z, TIAN Y, SHI Y. Robust twin support vector machine for pattern classification[J]. Pattern recognition, 2013, 46(1):305-316.
[12] PENG X, XU D. Bi-density twin support vector machines for pattern recognition[J]. Neurocomputing, 2013, 99(1):134-143.
[13] YE Q, ZHAO C, GAO S, et al. Weighted twin support vector machines with local information and its application[J]. Neural networks the official journal of the international neural network society, 2012, 35(11):31-39.
[14] FUNG G, MANGASARIAN O L. Proximal support vector machine classifiers[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2001:77-86.
[15] MANGASARIAN O L, WILD E W. Multisurface proximal support vector machine classification via generalized eigenvalues[J]. IEEE transactions on pattern analysis and machine intelligence, 2005, 28(1):69-74.
[16] JAYADEVA, KHEMCHANDANI R. Twin support vector machines for pattern classification[J]. IEEE transactions on pattern analysis and machine intelligence, 2007, 29(5):905-910.
[17] KUMAR M A, GOPAL M. Least squares twin support vector machines for pattern classification[J]. Expert systems with applications, 2009, 36(4):7535-7543.
[18] MELESSE A M, JORDAN J D. A comparison of fuzzy vs. augmented-ISODATA classification algorithms for cloud-shadow discrimination from Landsat images[J]. Photogrammetric engineering & remote sensing, 2002, 68(9):905-911.
[19] WAGSTAFF K, CARDIE C, ROGERS S, et al. Constrained K-means clustering with background knowledge[C]//Eighteenth international conference on machine learning. San Francisco:Morgan Kaufmann Publishers Inc. 2001:577-584.
[20] MANGASARIAN O L. Nonlinear programming[M]. SIAM, 1994:5-10.

相似文献/References:

[1]张文升,王立国,孟凡旺.基于嵌套窗口的高光谱图像目标检测[J].应用科技,2009,36(05):12.[doi:10.3969/j.issn.1009-671X.2009.05.004]
 ZHANG Wen-sheng,WANG Li-guo,MENG Fan-wang.Nested spatial window based target detection for hyperspectral images[J].Applied science and technology,2009,36(03):12.[doi:10.3969/j.issn.1009-671X.2009.05.004]
[2]赵春晖,王楠楠.基于背景抑制及顶点成分分析的[J].应用科技,2009,36(09):11.[doi:oi:10.3969/j.issn.1009-671X.2009.09.003]
 ZHAO Chun hui,WANG Nan nan.Anomaly detection of hyperspectral imagery based on background restrain and VCA[J].Applied science and technology,2009,36(03):11.[doi:oi:10.3969/j.issn.1009-671X.2009.09.003]
[3]王立国,邓禄群,张晶.改进的SGA端元选择的快速方法[J].应用科技,2010,37(04):19.[doi:1009-671X (2010) 04-0019-04]
 WANG Li-guo,DENG Lu-qun,ZHANG Jing.A fast endmember selection method based on simplex growing algorithm[J].Applied science and technology,2010,37(03):19.[doi:1009-671X (2010) 04-0019-04]
[4]王立国,赵妍,王群明.基于POCS的高光谱图像超分辨率方法[J].应用科技,2010,37(10):26.[doi:10.3969/j.issn.1009-671X.2010.10.007]
 WANG Li-guo,ZHAO Yan,WANG Qun-ming.POCS based super-resolution method for hyperspectral imagery[J].Applied science and technology,2010,37(03):26.[doi:10.3969/j.issn.1009-671X.2010.10.007]
[5]杨桄1,张俭峰1,赵波2,等.基于 PCA 和 KRX 算法的高光谱异常检测[J].应用科技,2014,41(05):11.[doi:10.3969 / j.issn.1009⁃671X.201312002]
 ,,et al.Anomaly detection based on PCA and KRX in hyperspectral images[J].Applied science and technology,2014,41(03):11.[doi:10.3969 / j.issn.1009⁃671X.201312002]
[6]王霖郁,刘一博.基于光谱角匹配加权的高光谱图像异常检测[J].应用科技,2017,44(06):20.[doi:10.11991/yykj.201610010]
 WANG Linyu,LIU Yibo.Anomaly detection for hyperspectral image based on weighted spectral angle match[J].Applied science and technology,2017,44(03):20.[doi:10.11991/yykj.201610010]
[7]崔颖,王雪婷,刘述彬,等.基于改进SPA算法的高效降维方法[J].应用科技,2018,45(05):51.[doi:10.11991/yykj.201712002]
 CUI Ying,WANG Xueting,LIU Shubin,et al.An efficient reduction method based on the improved successive projection algorithm[J].Applied science and technology,2018,45(03):51.[doi:10.11991/yykj.201712002]

备注/Memo

备注/Memo:
收稿日期:2016-6-16。
基金项目:国家自然科学基金项目(61675051);国家教育部博士点基金资助项目(20132304110007);黑龙江省自然科学基金项目(F201409).
作者简介:王立国(1974-),男,教授,博士生导师;杜心平(1990-),女,硕士研究生.
通讯作者:王立国,E-mail:wangliguo@hrbeu.edu.cn
更新日期/Last Update: 2017-07-07