A method for cloud removal in remote sensing images based on attention mechanism and residual symmetric connection structure

Kang Guan; Qing Cheng; Fan Ye; Haorui Wang

doi:10.1117/12.2688435

7 March 2024 A method for cloud removal in remote sensing images based on attention mechanism and residual symmetric connection structure

Kang Guan, Qing Cheng, Fan Ye, Haorui Wang

Author Affiliations +

Proceedings Volume 13088, MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 1308802 (2024) https://doi.org/10.1117/12.2688435
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China

Abstract

Optical remote sensing images have become an important data source for studying the characteristics of the Earth’s surface by their wide coverage, high spatial and temporal resolution, and information richness. However, in practical applications, optical remote sensing images are often affected by cloud occlusion, resulting in loss of surface information and errors. To this end, we propose a generative adversarial network (AM-RSC-GAN) based on the attention mechanism and residual symmetric connectivity structure. AM-RSC-GAN uses a multi-scale feature fusion module and the channel spatial attention mechanism to effectively improve the network’s extraction and fusion of global and local information of the feature image and adopts the residual symmetric connectivity structure to enabling the network to achieve a good performance between effectively removing the cloud layer and retaining the original detail information. The residual symmetric connection structure enables the network to achieve a good balance between effective cloud removal and preservation of original detail information, better preserving the detail information of the original image and achieving faster convergence speed. We experimentally compare with several previous cloud removal models on the open-source RICE dataset and the WHUS2-CR dataset. The results show that AM-RSC-GAN achieves better results in both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), proving that the model has excellent cloud removal performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kang Guan, Qing Cheng, Fan Ye, and Haorui Wang "A method for cloud removal in remote sensing images based on attention mechanism and residual symmetric connection structure", Proc. SPIE 13088, MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1308802 (7 March 2024); https://doi.org/10.1117/12.2688435

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KEYWORDS

Clouds

Remote sensing

Ocean optics

Feature fusion

Image fusion

Hydrology

Meteorology

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