Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images"

TL;DR

This paper reviews existing multitemporal cloud removal methods in remote sensing images and introduces a novel spatiotemporal fusion technique with poisson adjustment to improve accuracy in dynamic scenes.

Contribution

It provides a comprehensive comparison of current methods and proposes a new fusion approach that enhances cloud removal performance in multi-sensor, multi-temporal remote sensing images.

Findings

The proposed method outperforms existing techniques in accuracy.

Spatiotemporal fusion effectively handles significant scene changes.

Experimental results validate the method's potential for practical applications.

Abstract

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.