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中文摘要：
针对高光谱图像波段多、存在噪声干扰等特点,提出了一种混合Contourlet和主成分分析（principal component analysis,简称PCA）变换的高光谱图像融合方法。首先将多个波段的高光谱图像进行Contourlet变换,得到系列多尺度、方向各异的子带系数,然后利用PCA变换对各子带系数分别进行自适应融合处理。实验结果表明该算法可以有效地进行高光谱图像融合,消除噪声干扰,获得比直接应用Contourlet变换和PCA变换更好的融合效果。
英文摘要：
According to the characteristics of hyperspectral images such as multi-band and corrupted by various noises, a novel fusion method of hyperspectral images based on hybrid Contourlet transform and principal component analysis（PCA） transform were proposed. Firstly, Contourlet transform was performed simultaneously on several hyperspectral images to retain the sub-band images at different frequency and directions. Then each sub-band image was adaptively fused by PCA transform. The experiment result shows that the proposed method could well fuse hyperspectral images with noises eliminated and it outperforms the Contourlet and PCA methods.
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