Data fusion has been widely applied in the remote sensing research field. Principal component analysis (PCA) is one of the standard methods for data fusion. In this paper, a new algorithm--adding wavelet coefficients principal component analysis (AWPCA) is presented, which is based on principal component analysis (PCA) and is gotten from combining PCA and wavelet transform. The experimental results demonstrate that the higher quality image is obtained by AWPCA than by IHS and PCA mergers. AWPCA can be also applied in other fields where the high-resolution image is required.