Cloud removal by fusing multi-source and multi-temporal images

Remote sensing images often suffer from cloud cover. Cloud removal is required in many applications of remote sensing images. Multitemporal-based methods are popular and effective to cope with thick clouds. This paper contributes to a summarization and experimental comparison of the existing multitemporal-based methods. Furthermore, we propose a spatiotemporal-fusion with poisson-adjustment method to fuse multi-sensor and multitemporal images for cloud removal. The experimental results show that the proposed method is able to obtain more accurate results than the current multitemporal-based methods, especially when the multi-temporal images suffer from significant changes.