基于小波变换和MNF变换的遥感影像融合

Mergence of Remote Sensing Images Based on the Wavelet and MNF Transformations

随着高分辨率遥感卫星的产生,传统的融合技术难以达到较好的融合效果,如主成分分析(Principal Com-ponents Analysis,PCA)变换融合对噪声比较敏感,受到融合区域的限制,而最小噪声分离(Minimum Noise Frac-tion,MNF)变换考虑了噪声和融合区域,是一种完备的成分分解方法,小波变换(Wavelet Transformation,WT)融合也存在一定程度的光谱失真。由此,本文在分析MNF变换和WT的基础上,以IKONOS新型高分辨率观测卫星提供的全色和多光谱数据为实验数据,提出了一种将两者相结合的遥感影像融合方法,通过与其它融合方法的定量和视觉比较,发现该方法能得到更好的融合效果。

As the high-resolution remote sensing satellite has emerged, it is very difficult for the traditional image mergence techniques to get good mergence results. For example, the mergence method with principal component a- nalysis （PCA） transformation is more sensitive to the noise and limited by the mergence region. But, the method with the minimum noise fraction （MNF） transformation is a self-contained component analysis method, with the effect of noise and mergence region eonsidered. The wavelet transformation （WT） mergence also, to some extent, brings color distortion. Therefore, on the basis of the MNF and WT analyses, a new remote sensing mergence method integrated wavelet with MNF transformation is studied where IKONOS multi-spectral image and panchro marie image are used in the paper. The results demonstrate that the new method could get better mergence effect by visually and quantitatively comparing with other mergence methods.