Fusing a Hyperspectral image (HSI) and a multispectral image (MSI) from different sensors is an economic and effective approach to get an image with both high spatial and spectral resolution, but localized changes between the multiplatform images can have negative impacts on the fusion. In this letter, we propose a novel sparsity constrained fusion method (SCFus) to fuse multiplatform HSIs and MSIs based on matrix factorization. Specifically, we imposed $\ell 1$ norm on the residual term of the MSI to account for the localized changes between the hyperspectral and MSIs. Furthermore, we plugged a state-of-the-art denoiser, namely block-matching and 3-D filtering (BM3D), as the prior of the subspace coefficients by exploiting the plug-and-play framework. We refer to the proposed method as SCFus for hyperspectral and MSIs. Experimental results suggest that the proposed fusion method is more effective in fusing hyperspectral and MSIs than the competitors.