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

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

doi:10.3969/j.issn.0372-2112.2014.01.013

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

更新时间：2025-07-16

- Dimensionality Reduction of Remote Sensing Image Using Semi-Supervised Discriminative Locality Alignment
- Acta Electronica Sinica Vol. 42, Issue 1, Pages: 84-88(2014)
- 作者机构：
  
  中国矿业大学信息与电气工程学院,江苏,徐州,221116
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61072094, No.61273143);Program for New Century Excellent Talents in University of Ministry of Education of China (No.NCET-08-0836, No.NCET-10-0765);Ph.D. Programs Foundation of Ministry of Education of China (No.20110095110016, No.20120095110025);Young Teachers Fund of Fok Ying-Tong Education Foundation (No.121066)
- DOI：10.3969/j.issn.0372-2112.2014.01.013
  CLC： TP751.1
- Published：2014
- 稿件说明：
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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.
DOI：

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. DOI： 10.3969/j.issn.0372-2112.2014.01.013.

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.

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.

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