A novel cloud removal method by fusing features from SAR and neighboring optical remote sensing images - Yuyao Wang; Jiehai Cheng (PLoS ONE, 2025)
Optical remote sensing images were prone to extensive cloud coverage, especially under mountainous conditions with frequent weather changes. To address this issue, this paper proposed a novel cloud removal method that integrated features from SAR and neighboring optical remote sensing images. The method was based on a deep CGAN network, leveraging both local and global features of SAR images as well as edge features of optical remote sensing images to perform coarse cloud removal. Building upon the coarse cloud removal, the spectral features of neighboring optical remote sensing images were utilized for refined cloud removal. The experimental results showed that the proposed coarse-to-fine cloud removal method achieved a satisfactory cloud removal performance for optical remote sensing images. The RMSE, SAM, mSSI, and CC values were 0.0391, 0.0729, 0.9221, and 0.9537, respectively. Compared with other classical methods, each of these four metrics improved by at least 0.0119, 0.0438, 0.0217, and 0.0240, respectively. Moreover, the proposed method demonstrated superior performance in terms of boundary smoothness, cloud shadow elimination, spectral consistency, and spatial detail restoration for cloud removal in images with different underlying surface conditions. The effectiveness of the proposed method will improve the quality of cloud removal in optical remote sensing images.