Fusion of multispectral and panchromatic satellite images using Principal Component Analysis and Nonsubsampled Contourlet Transform

A novel fusion scheme is proposed for multispectral (MS) and panchromatic (PAN) satellite images using Principal Component Analysis (PCA) and Nonsubsampled Contourlet Transform (NSCT). This scheme first performs PCA on MS, and NSCT on PAN and the first principal component (PC¹) to get corresponding low-frequency and high-frequency coefficients, then fuses the approximation coefficients using PCA again for the tradeoff between the spectral and spatial information, and fuses the subbands coefficients based on local variance for the spatial detail information, finally a fused image is formed through inverse NSCT and inverse PCA. Experimental results show that the proposed fusion scheme can effectively preserve spectral information while improving the spatial quality, and outperforms the general IHS-, PCA-, wavelet-, contourlet-based fusion methods.