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A cloud removal approach based on information cloning is introduced. The approach removes cloud-contaminated portions of a satellite image and then reconstructs the information of missing data utilizing temporal correlation of multitemporal images. The basic idea is to clone information from cloud-free patches to their corresponding cloud-contaminated patches under the assumption that land covers change insignificantly over a short period of time. The patch-based information reconstruction is mathematically formulated as a Poisson equation and solved using a global optimization process. Thus, the proposed approach can potentially yield better results in terms of radiometric accuracy and consistency compared with related approaches. Some experimental analyses on sequences of images acquired by the Landsat-7 Enhanced Thematic Mapper Plus sensor are conducted. The experimental results show that the proposed approach can process large clouds in a heterogeneous landscape, which is difficult for cloud removal approaches. In addition, quantitative and qualitative analyses on simulated data with different cloud contamination conditions are conducted using quality index and visual inspection, respectively, to evaluate the performance of the proposed approach.