Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images"
This work tackles cloud cover issues in remote sensing applications, but it is incremental as it builds on existing multitemporal methods.
The paper addresses cloud removal in remote sensing images by summarizing and comparing existing multitememporal methods and proposing a spatiotemporal-fusion with poisson-adjustment method to improve accuracy in images with significant changes.
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 comparation of the existing multitemporal-based methods. Furthermore, we propose a spatiotemporal-fusion with poisson-adjustment method to fuse multi-sensor and multi-temporal images for cloud removal. The experimental results show that the proposed method has potential to address the problem of accuracy reduction of cloud removal in multi-temporal images with significant changes.