Removal of thin clouds from high-resolution optical images based on multiscale feature fusion

Yunlong Xiao

doi:10.1117/12.3040690

10 September 2024 Removal of thin clouds from high-resolution optical images based on multiscale feature fusion

Yunlong Xiao

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 1325702 (2024) https://doi.org/10.1117/12.3040690
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

High-resolution optical images are susceptible to atmospheric influences during their formation, and thin clouds are the most important influencing factor. Feature information loss due to thin-cloud coverage is a common problem. To solve this problem, a thin cloud removal method based on multi-scale feature fusion for high-resolution optical remote sensing images is proposed; this method can capture more image details. The multi-scale feature fusion module is introduced into the improved structure of the generative network model. By fusing multiple image scale information, the model can enhance the ability of extracting features such as shape, texture and colour. Experimental results on high-resolution remote sensing dataset show that the method achieves 29.4758 and 0.8902 for the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), respectively. Comparing the method with existing methods, it proves that the method has a more accurate effect in remote sensing imagery thin cloud removal.

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

Citation Download Citation

Yunlong Xiao "Removal of thin clouds from high-resolution optical images based on multiscale feature fusion", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 1325702 (10 September 2024); https://doi.org/10.1117/12.3040690

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KEYWORDS

Clouds

Image fusion

Remote sensing

Feature fusion

Feature extraction

Ocean optics

Deep learning

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