Cloud Removal Based on Sparse Representation via Multitemporal Dictionary Learning
Cloud covers, which generally appear in optical remote sensing images, limit the use of collected images in many applications. It is known that removing these cloud effects is a necessary preprocessing step in remote sensing image analysis. In general, auxiliary images need to be used as the reference images to determine the true ground cover underneath cloud-contaminated areas. In this paper, a new cloud removal approach, which is called multitemporal dictionary learning (MDL), is proposed. Dictionaries of the cloudy areas (target data) and the cloud-free areas (reference data) are learned separately in the spectral domain. The removal process is conducted by combining coefficients from the reference image and the dictionary learned from the target image. This method could well recover the data contaminated by thin and thick clouds or cloud shadows. Our experimental results show that the MDL method is effective in removing clouds from both quantitative and qualitative viewpoints.