An efficient pan-sharpening method via a combined adaptive PCA approach and contourlets
An efficient pan-sharpening method via a combined adaptive PCA approach and contourlets
复制标题
DOI：
10.1109/tgrs.2008.916211
复制
发表时间：
2008-05-01
影响因子：
8.2
通讯作者：
King, Roger L.
中科院分区：
文献类型：
--
作者：
Shah, Vijay P.;Younan, Nicolas H.;King, Roger L.
多光谱图像中相邻像素在空间和光谱上的高度相关性使得在执行全色锐化之前有必要使用有效的数据变换方法。小波和主成分分析 (PCA) 方法分别是空间和光谱变换的流行选择。当前基于 PCA 的全色锐化方法假设高方差的第一主成分 (PC) 是用高分辨率直方图匹配全色 (PAN) 图像中的高空间细节替换或注入它的理想选择。本文提出了一种用于全色锐化的组合自适应PCA-轮廓波方法，其中自适应PCA用于减少光谱失真，并结合使用非下采样轮廓波在全色锐化中进行空间变换，以克服小波在有效表示方向信息和捕获物体的内在几何结构方面的局限性。通过对高分辨率（IKONOS 和 QuickBird）和中等分辨率（Landsat-7 增强型专题制图器 Plus）数据集进行全色锐化来测试所提出方法的效率。使用全局验证指标对全色锐化图像的评估表明，自适应PCA方法有助于减少光谱失真，并且其与轮廓波的合并提供了更好的融合结果。
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.