Missing data reconstruction (e.g., dead pixel repair and cloud removing) in remote sensing images is a very important problem for the subsequent image analysis. It is well-known that missing data reconstruction is an ill-posed problem. In remote sensing images, there is a strong correlation in spectral frequencies or in temporal frames, and also there are a lot of self-similarity patterns in spatial domain. We can make use of these properties to derive low rank matrices according to their spectral, temporal and spatial dimensions. In this paper, we propose a tensor completion model based on these low rank matrices to deal with missing data reconstruction problem. We also present a weighting method for spectral, temporal and spatial dimensions and for their distribution of singular values. Our experimental results demonstrate that the weighting method can recover remote images very well. In particular, we show the effectiveness of the proposed method for both simulated and real data sets, and the performance of the proposed in terms of visual and quantitative measures is better than those of the other testing methods.