In this paper,we propose a spatially weighted collaborative sparse unmixing method aiming at fully exploiting the spatial information in the hyperspectral images,in which a collaborative sparse regularizer is used to describe the row sparsity of the abundance,while on the top of the collaborative regularizer,a spatial weighting factor introducing the spatial correlations is incorporated.The proposed model is optimized by the well known alternating direction method of multiplier.Our experimental results,conducted using both simulated and real hyperspectral data sets,illustrate the good potential of the proposed algorithm which can greatly improve the abundance estimation results when compared with other advanced sparse unmixing methods.