A remote sensing image fusion method based on the matting model is presented. The matting model refers to each band of the hyperspectral (HS) image that can be decomposed into three components, i.e., alpha channel, spectral foreground, and spectral background. First, the panchromatic (PAN) is sharpened to enhance the spatial details and the intensity component of the HS image is obtained by the nonlinear synthesis method. Different from the traditional matting model-based method in which the PAN image serves as the alpha channel, we subsequently transform the sharpened PAN image and the intensity component to the principal components analysis domain to obtain the first principal components channel. The first principal components channel is selected as the alpha channel of the HS image. The selected alpha channel contains most of the spatial information of both the PAN and HS images. Finally, the HS foreground and HS background are estimated by the alpha channel, and the fused HS image can be reconstructed perfectly. Experimental results reveal that the proposed method is superior to the existing state-of-the-art methods.