Cloud-contaminated image reconstruction with contextual spatio-spectral information

Recently, a novel general method for the contextual reconstruction of cloud-contaminated areas in multitemporal multispectral images has been proposed in the literature. Though promising, this method does not fully exploit all available information, thus leaving room for further methodological enhancements. The aim of this work is to improve the contextual reconstruction process by opportunely capturing spatial and spectral correlations characterizing the considered image. In addition, we propose a novel procedure to generate an error map for providing the end-user with helpful indications about the reconstruction reliability. Thorough experiments conducted on a multitemporal sequence of Landsat-7 ETM+ images are reported and discussed.