Two-Pass Robust Component Analysis for Cloud Removal in Satellite Image Sequence

Abstract

Due to the inevitable existence of clouds and their shadows in optical remote sensing images, certain ground-cover information is degraded or even appears to be missing, which limits analysis and utilization. Thus, cloud removal is of great importance to facilitate downstream applications. Motivated by the sparse representation techniques which have obtained a stunning performance in a variety of applications, including target detection, anomaly detection, and so on; we propose a two-pass robust principal component analysis (RPCA) framework for cloud removal in the satellite image sequence. First, a plain RPCA is applied for initial cloud region detection, followed by a straightforward morphological operation to ensure that the cloud region is completely detected. Subsequently, a discriminative RPCA algorithm is proposed to assign aggressive penalizing weights to the detected cloud pixels to facilitate cloud removal and scene restoration. Significantly superior to currently available methods, neither a cloud-free reference image nor a specific algorithm of cloud detection is required in our method. Experiments on both simulated and real images yield visually plausible and numerically verified results, demonstrating the effectiveness of our method.