Multi-Temporal Landsat Data Automatic Cloud Removal Using Poisson Blending

Abstract

Cloud and cloud shadow are common issues in optical satellite imagery, which greatly reduce the usage of data archive. As for the Landsat data, great advances have been made on detecting cloud and cloud shadow. However, few studies were performed on Landsat cloud removal for large areas. To facilitate land cover dynamics studies with high temporal resolution, we present an automatic cloud removal algorithm in this paper. Specifically, For Landsat Collection 1 Level-1 surface reflectance products, the algorithm first builds a cloud mask from the Quality Assessment (QA) band, and then reconstructs cloud-contaminated portions based on multi-temporal Landsat images with temporal similarity. To further eliminate radiation differences between cloud-free and reconstructed regions, a Poisson blending algorithm is adopted. Besides, the efficiency of gradient-domain compositing is accelerated by the quad-tree approach. Experiments have been performed to process more than 50,000 Landsat 8 Operational Land Imager (OLI) images covering China from 2013 to 2017, which yield promising results in terms of radiometric accuracy and consistency for experimental images with cloud coverage less than 80%. The produced Landsat time series images with cloud removal can be further used for analyzing land cover and land change dynamics in China, and the proposed algorithm should be easily employed to produce cloud-free Landsat time series for other areas.