An efficient pan-sharpening method via a combined adaptive PCA approach and contourlets

被引:528
作者
Shah, Vijay P. [1 ,2 ]
Younan, Nicolas H. [1 ,2 ]
King, Roger L. [1 ,2 ]
机构
[1] Mississippi State Univ, Dept Elect & Comp Engn, Mississippi State, MS 39762 USA
[2] Mississippi State Univ, GeoResources Inst, Mississippi State, MS 39762 USA
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2008年 / 46卷 / 05期
关键词
contourlets; image fusion; pan-sharpening; principal component analysis (PCA); wavelets;
D O I
10.1109/TGRS.2008.916211
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
High correlation among the neighboring pixels both spatially and spectrally in a multispectral image makes it necessary to use an efficient data transformation approach before performing pan-sharpening. Wavelets and principal component analysis (PCA) methods have been a popular choice for spatial and spectral transformations, respectively. Current PCA-based pan-sharpening methods make an assumption that the first principal component (PC) of high variance is an ideal choice for replacing or injecting it with high spatial details from the high-resolution histogram-matched panchromatic (PAN) image. This paper presents a combined adaptive PCA-contourlet approach for pan-sharpening, where the adaptive PCA is used to reduce the spectral distortion and the use of nonsubsampled contourlets for spatial transformation in pan-sharpening is incorporated to overcome the limitation of the wavelets in representing the directional information efficiently and capturing intrinsic geometrical structures of the objects. The efficiency of the presented method is tested by performing pan-sharpening of the high-resolution (IKONOS and QuickBird) and the medium-resolution (Landsat-7 Enhanced Thematic Mapper Plus) datasets. The evaluation of the pan-sharpened images using global validation indexes reveal that the adaptive PCA approach helps reducing the spectral distortion, and its merger with contourlets provides better fusion results.
引用
收藏
页码:1323 / 1335
页数:13
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