基于PCA和NSCT变换的遥感图像融合方法

摘要

In order to improve the problem of the lack of detailed information in expression of image using non-subsampled Contourlet transform (NSCT), this paper proposes an improved method based on principal component analysis (PCA) and NSCT transform remote sensing image fusion. Firstly, PCA transform is applied to the low spatial resolution multi-spectral (MS) image, and then the first principal component (PC1) is extracted. Secondly, NSCT transform is applied to the PC1 and the high spatial resolution panchromatic (PAN) image. For the low frequency coefficients of the above two, the rules of wavelet transform fusion are used, and for the high frequency coefficients the adaptive weighted fusion rules based on region standard deviation are used. Finally, we get the fusion image by using inverse NSCT transform and inverse PCA transform. The results show that the method combines the detail information of the source image effectively, and also get better visual effect and better evaluation index.%为了改善非下采样Contourlet变换(NSCT)在图像细节信息表达的缺失问题,提出了一种新的基于主成分分析(PCA)和NSCT的遥感图像融合方法.首先对低空间分辨率多光谱(MS)图像进行PCA变换,提取第一主分量(PC1);其次,对PC1和高空间分辨率全色(PAN)图像进行NSCT变换,对二者的低频系数采用小波变换的融合规则,高频系数采用基于区域标准差自适应加权的融合规则;最后,经过PCA逆变换和NSCT逆变换得到融合图像.仿真实验结果表明,该方法不仅有效地融合了源图像的细节信息,而且得到了较好的视觉效果和较优的评价指标.