Reconstructing Missing Information of Remote Sensing Data Contaminated by Large and Thick Clouds Based on an Improved Multitemporal Dictionary Learning Method

The presence of clouds and cloud shadows has limited the applications of optical remote sensing data. Currently, most cloud removal methods are focused on reconstructing remote sensing data contaminated by small or thin clouds. This study proposes an improved method based on multitemporal dictionary learning to reconstruct missing information of remote sensing data contaminated by large and thick clouds. First, the contaminated target image is initialized using all available adjacent cloud-free reference images. Second, reconstructed images from each of the reference images are produced using dictionary learning and sparse representation methods. Then, weights are determined for the abovementioned reconstructed images based on their reconstruction errors over uncontaminated regions and are used to generate the preliminary reconstruction result. Finally, an error correction step for the contaminated regions is applied to the preliminary result, which is then combined with the original uncontaminated pixels to produce the final reconstruction result. The proposed method was evaluated on simulated clouds/cloud shadows based on remote sensing data with various sizes and land cover types. Visual and quantitative analyses of the reconstruction results show that the proposed method outperformed the generally used geostatistical neighborhood similar pixel interpolator (GNSPI) and nonnegative matrix factorization and error correction (S-NMF-EC) methods. Therefore, the results indicated that the proposed method was capable of accurately and effectively reconstructing data contaminated by large and thick clouds.