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Title: Multi-scale adaptive graph convolution-based thick cloud removal method for optical remote sensing images

Authors: Honghua Qiu; Kaili Zhang

Addresses: School of Applied Technology, Zhumadian Preschool Education College, Zhumadian, Henan, 463000, China ' Henan Institute of Surveying and Mapping, Zhengzhou, Henan, 450003, China

Abstract: Cloud occlusion challenges remote sensing image processing by degrading quality and analysis accuracy. Existing cloud removal methods capture local features but struggle with global dependencies and cloud morphology, limiting detail restoration and consistency. To address this, we propose a multi-scale adaptive graph convolution generative adversarial network (MAGC-GAN), integrating a multi-scale adaptive graph convolution network (MAGCN) and an adaptive patch discriminator (APD). MAGCN enhances spatial dependencies using adaptive graph convolution, effectively reconstructing cloud-covered regions by capturing global contextual relationships. A multi-scale feature fusion mechanism enables adaptation to varying cloud thicknesses. APD improves fine-detail recovery by evaluating multiple local patches individually, using an adaptive affine transformation matrix. It also incorporates texture-aware and global consistency losses to restore high-frequency details while maintaining coherence. Compared to existing methods, MAGC-GAN significantly enhances cloud-occluded region restoration, particularly in detail recovery and precise cloud edge reconstruction.

Keywords: remote sensing image; cloud removal; graph convolutional network; GCN; generative adversarial network; GAN; adaptive.

DOI: 10.1504/IJICT.2025.146369

International Journal of Information and Communication Technology, 2025 Vol.26 No.15, pp.57 - 77

Received: 10 Mar 2025
Accepted: 28 Mar 2025
Published online: 27 May 2025 *