遥感影像的半监督判别局部排列降维

doi:10.3969/j.issn.0372-2112.2014.01.013

电子学报 ›› 2014, Vol. 42 ›› Issue (1): 84-88.DOI: 10.3969/j.issn.0372-2112.2014.01.013

遥感影像的半监督判别局部排列降维

王雪松, 胡汇涓, 程玉虎

中国矿业大学信息与电气工程学院, 江苏徐州 221116

收稿日期:2012-11-19 修回日期:2013-01-21 出版日期:2014-01-25
- 作者简介:
- 王雪松 女，1974年生于安徽泗县，2002年获中国矿业大学控制理论与控制工程专业博士学位，现为中国矿业大学信息与电气工程学院教授、博士生导师.主要研究方向为机器学习与模式识别、遥感信息处理等. E-mail:wangxuesongcumt@163.com 胡汇涓 女，1986年生于河南泌阳，2010年获中国矿业大学学士学位，现为中国矿业大学控制理论与控制工程专业硕士研究生，研究方向为遥感信息处理. E-mail:huhuijuan1012@sina.com 程玉虎 男，1973年生于安徽淮南，2005年获中国科学院自动化研究所控制理论与控制工程专业博士学位，现为中国矿业大学信息与电气工程学院教授、博士生导师.主要研究方向为机器学习与模式识别、智能系统等. E-mail:chengyuhu@163.com
- 基金资助:
- 国家自然科学基金 (No.61072094，No.61273143）; 教育部新世纪优秀人才支持计划 (No.NCET-08-0836，No.NCET-10-0765）; 教育部博士点基金 (No.20110095110016，No.20120095110025）; 霍英东教育基金会青年教师基金 (No.121066）

Dimensionality Reduction of Remote Sensing Image Using Semi-Supervised Discriminative Locality Alignment

WANG Xue-song, HU Hui-juan, CHENG Yu-hu

School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China

Received:2012-11-19 Revised:2013-01-21 Online:2014-01-25 Published:2014-01-25

摘要/Abstract

摘要： 针对遥感影像数据具有的高维数和少量已标记样本的特性，提出一种基于图的半监督判别局部排列降维方法.首先，针对全部已标记和未标记样本数据构造相似图和惩罚图.然后，基于同类近邻点的分散度最小且不同类近邻点的分散度最大的原则，分别确立相似图和惩罚图上的优化目标.最后，通过同时优化这两种图上的目标函数，得到从高维到低维的最优映射关系，从而达到对高维遥感影像数据维数约简的目的.ROSIS高光谱数据上的实验结果表明，所提算法能够有效提高高维遥感影像的总体精度和Kappa系数.

关键词: 半监督, 判别局部排列, 图, 降维, 遥感影像

Abstract: Aiming at remote sensing image data having properties of high-dimension and small amount of labeled samples,a dimensionality reduction algorithm called semi-supervised discriminative locality alignment based on graph is proposed.At first,a similarity graph and a penalty graph are constructed according to all labelled and unlabelled samples.Then,based on the principle that the dispersion between neighbours of the same class is minimum and that the dispersion between neighbours of different class is maximum,optimization goals on the similarity graph and on the penalty graph are respectively determined.At last,an optimal mapping from the high-dimensional space to a low-dimensional subspace can be obtained by simultaneously optimizing the two objective functions,which makes the dimensionality reduction of high-dimensional remote sensing images realized.Experimental results on ROSIS hyperspectral data show that the proposed algorithm can effectively improved the overall accuracy and Kappa coefficient of high-dimensional remote sensing images.

Key words: semi-supervised, discriminative locality alignment, graph, dimensionality reduction, remote sensing image

中图分类号:

TP751.1

王雪松, 胡汇涓, 程玉虎. 遥感影像的半监督判别局部排列降维[J]. 电子学报, 2014, 42(1): 84-88.

WANG Xue-song, HU Hui-juan, CHENG Yu-hu. Dimensionality Reduction of Remote Sensing Image Using Semi-Supervised Discriminative Locality Alignment[J]. Acta Electronica Sinica, 2014, 42(1): 84-88.

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遥感影像的半监督判别局部排列降维

Dimensionality Reduction of Remote Sensing Image Using Semi-Supervised Discriminative Locality Alignment

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