Many pansharpening algorithms are based on the principle of extracting spatial details from panchromatic (PAN) images and injecting them into multispectral (MS) images. In this paper, we present two fusion approach based on same principle by integrating standard principle component analysis (PCA) with decimated and undecimated rotated wavelet transform. When decimated/subsampled rotated wavelet transform (SSRWT) is used for fusion of MS and PAN images, three visual artifacts get introduced in the fused image namely color distortion, shifting effect and shift distortion. To eliminate color distortion, SSRWT is integrated with standard PCA, i.e., PCA–SSRWT. Color distortion is significantly mitigated, but shifting effect and shift distortion persist in the fused image of PCA–SSRWT. After employing undecimated/nonsubsampled rotated wavelet transform (NSRWT), shifting effect and shift distortion get eliminated with minimum color distortion. However, fused image as a result of NSRWT is spectrally high but spatially low. In order to improve spatial quality and remove visual artifacts observed in SSRWT and PCA–SSRWT, NSRWT is integrated with standard PCA, i.e., PCA–NSRWT. Visual and quantitative analysis is carried out to validate the quality of fused image for all the algorithms. Visual interpretation suggests that fused image obtained using PCA–NSRWT is superior to fused images of SSRWT, PCA and NSRWT. The overall quantitative analysis manifests that the PCA–NSRWT is consistent with visual interpretation and performs better than state-of-the-art methods. PCA–NSRWT not only removes visual artifacts but also improves spectral and spatial quality of the fused image compared to individual PCA, SSRWT, NSRWT and PCA–SSRWT. Based on visual and quantitative analysis, it is observed that PCA works better with undecimated compared to decimated rotated wavelet transform for fusion.

许多全锐化算法基于从全色（PAN）图像中提取空间细节并将其注入多光谱（MS）图像的原理。在本文中，我们通过将标准主成分分析（PCA）与抽取和未抽取旋转小波变换相结合，提出了两种基于相同原理的融合方法。当使用抽取/二次采样旋转小波变换（SSRWT）融合MS和PAN​​图像时，融合图像中引入了三个视觉伪像，即颜色失真，移位效果和移位失真。为了消除颜色失真，SSRWT与标准PCA（即PCA–SSRWT）集成在一起。显着减轻了色彩失真，但在PCA–SSRWT的融合图像中仍然存在偏移效果和偏移失真。在使用未抽取/未抽样的旋转小波变换（NSRWT）之后，以最小的色彩失真消除了偏移效果和偏移失真。但是，由于NSRWT而产生的融合图像在光谱上较高，但在空间上较低。为了提高空间质量并消除在SSRWT和PCA–SSRWT中观察到的视觉伪影，NSRWT与标准PCA（即PCA–NSRWT）集成在一起。进行视觉和定量分析以验证所有算法的融合图像质量。视觉解释表明，使用PCA–NSRWT获得的融合图像优于SSRWT，PCA和NSRWT的融合图像。总体定量分析表明，PCA–NSRWT与视觉解释一致，并且比最先进的方法执行得更好。与单独的PCA，SSRWT，NSRWT和PCA–SSRWT相比，PCA–NSRWT不仅可以消除视觉伪影，而且可以改善融合图像的光谱和空间质量。基于可视化和定量分析，可以观察到PCA在未抽样的情况下与融合的抽样旋转小波变换相比效果更好。