In order to preserve both spectral and spatial information simultaneously in fused image, we introduce the minimum Hausdorff distance and NonSampled Shearlet Transform (NSST) to construct a new method for remote sensing image fusion. Firstly, Principal Component Analysis (PCA) transform is applied in the original multispectral image to obtain the first principal component, this component and the panchromatic image are decomposed by NSST respectively to obtain the corresponding low frequency subband coefficients and high frequency subband coefficients. Then, the low frequency subband coefficients are fused by sparse representation, the sparse coefficients of sparse representation are fused with the region space frequency; for the high frequency subband coefficients, the regional structure similarity is utilized, using the minimum Hausdorff distance to represent the correlation of regions and different fusion strategies are adopted according to the correlation. Finally, the fused coefficients are transformed by inverse NSST to obtain the new principal component, the new component and other higher order principal components are transformed by inverse PCA transform to obtain the fused image. In this paper, three QuickBird satellite images and one SPOT satellite image are selected for testing, the results show that compared with the traditional fusion strategy algorithms, the fusion results obtained by proposed method have better objective evaluation index and subjective visual effect.