Cloud effects removal via sparse representation

Optical remote sensing images are often contaminated by the presence of clouds. The development of cloud effect removal techniques can maximize the usefulness of multispectral or hyperspectral images collected in the spectral range from visible to mid infrared. This paper presents a new data reconstruction technique, via dictionary learning and sparse representation, to remove the cloud effects. Dictionaries of the cloudy data (target data) and the cloud free data (reference data) are learned separately in the spectral domain, where each atom represents a fine ground cover component under the two imaging conditions. In this study, it is found that the sparse coefficients of the reference data are the true weightings of each atom, which can be used to replace the cloud affected coefficients to achieve data correction. Experiments were conducted using Landsat 8 OLI data sets downloaded from the USGS website. The testing results show that clouds of various thickness and cloud shadows can be removed effectively using the proposed method.