Missing Data Reconstruction for Remote Sensing Images with Weighted Low-Rank Tensor Model

Missing data reconstruction for remote sensing images, such as dead-pixel recovery and cloud removal, is important for remote sensing data applications. Missing information reconstruction is well known as being an ill-posed inverse problem. In this paper, a weighted low-rank tensor regularization model is proposed to handle the problem. The proposed model fully utilizes the correlations in the spatial, spectral, and temporal components of the remote sensing images to adaptively deal with the varied missing data problems, including the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) band 6 dead line problem, the Landsat scan-line corrector failure (SLC-off) problem, and cloud contamination. A double-weighted treatment is developed to balance the contributions from the different dimensions and preserve the different structures and textures in remote sensing images. The experiments undertaken confirmed the good performance of the proposed method, and the reconstruction results of the proposed method, in both visual effect and quantitative evaluation, were superior to those of the other methods.