Thick cloud removal of remote sensing images based on multi-reference image information fusion

doi:10.6046/zrzyyg.2021209

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Abstract

The cloud removal of remote sensing images is very important in the processing and analysis of remote sensing images and plays a crucial role in the subsequent image information extraction and other operations. Aiming at the high-quality requirements and low applicability of the reconstructed images in the cloud removal of multi-temporal remote sensing image fusion, a thick cloud removal algorithm based on one or more reference images was proposed, mainly including a selection of reference image, radiometric normalization, multi-temporal image fusion, and Poisson image editing. Firstly, the reference image was selected according to the image masking and the principal component information, and the radiometric normalization of the multi-source remote sensing image was carried out to preserve the change of ground feature information. Then, the image was fused based on the selective multi-source total variation model, and the boundary gradient discontinuity after image fusion was reduced by Poisson image editing. The experimental results show that the proposed method can effectively remove clouds from multi-source remote sensing images with thick clouds and different quality, and obtain higher image detail precision than traditional methods.

Keywords thick cloud removal reference image radiometric normalization multi-temporal selective multi-source total variation

ZTFLH:

TP79

Issue Date: 20 June 2022

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	Sili JIANG
	Wei HUANG
	Rui HUANG

Cite this article:

Sili JIANG,Wei HUANG,Rui HUANG. Thick cloud removal of remote sensing images based on multi-reference image information fusion[J]. Remote Sensing for Natural Resources, 2022, 34(2): 121-127.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021209 OR https://www.gtzyyg.com/EN/Y2022/V34/I2/121

Fig.1 Cloud removal flow chart of multi-temporal image fusion based on reference image

Fig.2-1 Selection of reference image

Fig.2-2 Selection of reference image

Tab.1 Reconstruction results of mountain area, city, river, road and other ground features

Tab.2 Quantitative index comparison based on the output results of each original image

Tab.3 Comparation of reconstruction results of mountain area, city, river, road and other ground features

Tab.4 Comparison of PSNR and SSIM values of different algorithms

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