Dimension reduction of remote sensing images by incorporating spatial and spectral properties

被引:9
作者
Dianat, R. [1 ]
Kasaei, S. [1 ]
机构
[1] Sharif Univ Technol, Dept Comp Engn, Image Proc Lab, Tehran, Iran
关键词
Dimension reduction; Remote sensing; Image analysis;
D O I
10.1016/j.aeue.2009.10.001
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A new and efficient dimension reduction method is introduced in this paper. The proposed method, almost the same as the well-known principal component analysis (PCA) method, enjoys the properties of uncorrelatedness of resulting components and orthogonality of transform coefficients. In addition, by incorporating spatial and spectral properties among image pixels, the method obtains more accurate classification results with less computational cost. (C) 2010 Published by Elsevier GmbH.
引用
收藏
页码:729 / 732
页数:4
相关论文
共 18 条
[1]  
[Anonymous], 2002, SURVEY DIMENSION RED
[2]  
[Anonymous], 2002, Principal components analysis
[3]  
[Anonymous], 1961, Adaptive Control Processes: a Guided Tour, DOI DOI 10.1515/9781400874668
[4]  
[Anonymous], 1994, Multidimensional Scaling
[5]  
Carreira-Perpinan M., 1997, CS9609 U SHEFF DEP C
[6]  
DIANAT R, 2009, IEEE T GEOSCI REMOTE, V47, P1
[7]  
Herik J., DIMENSIONALITY REDUC
[8]   Hyperspectral data analysis and supervised feature reduction via projection pursuit [J].
Jimenez, LO ;
Landgrebe, DA .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1999, 37 (06) :2653-2667
[9]   Feature reduction of hyperspectral imagery using hybrid wavelet-principal component analysis [J].
Kaewpijit, S ;
Le Moigne, J ;
El-Ghazawi, T .
OPTICAL ENGINEERING, 2004, 43 (02) :350-362
[10]   Automatic reduction of hyperspectral imagery using wavelet spectral analysis [J].
Kaewpijit, S ;
Le moigne, J ;
El-Ghazawi, T .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2003, 41 (04) :863-871