There is an increased utilization of image fusion techniques for the combination of multispectral bands with higher resolution panchromatic bands of to produce so-called pan-sharpened images with both high spatial and spectral resolution. The objective of this study is to evaluate performance and quality of the following pan-sharpening algorithms: i) Discrete Wavelet Transformation (DWT), ii) À trous Wavelet Transforms fusion (ATWT), iii) Gram-Schmidt Spectral Sharpening (GS) and iv) Principal Component Spectral Sharpening (PC). We intended to evaluate the spectral and spatial quality of the pan-sharpened image by using Universal Image Quality Index (UIQI), Correlation Coefficient (CC), Variance Texture Filter and Change Class Difference. The variance texture images represent the high frequency domain data .The difference of the two variance images was used to estimate the spatial quality of the sharpened images. The variance images subtraction shows that ATWT pan-sharpening obtains the best results followed with PC and GS, whereas DWT achieves the poorest result. Applying CC as quality measure exhibits the same results; again DWT obtained the poorest correlation coefficient. The UIQI results, calculated for different land-cover classes, indicate that ATWT pan-sharpening achieves a good spectral and spatial quality compared to DWT. The percentage of changed classes indicates that both wavelet-based algorithms (ATWT and DWT) provide better quality than PC and GS pan-sharpening. Therefore the wavelet-based techniques seem to better preserve the spectral information of the original multispectral bands, whereas PC and GS come along with a larger modification of the spectral information.