Balancing Colors of Nonoverlapping Mosaicking Images With Generative Adversarial Networks

被引:3
作者
Ma, Yaobin [1 ,2 ]
Wei, Jingbo [1 ]
Huang, Xiangtao [3 ]
机构
[1] Nanchang Univ, Inst Space Sci & Technol, Nanchang 330031, Jiangxi, Peoples R China
[2] Nanchang Univ, Sch Resources Environm & Chem Engn, Nanchang 330031, Jiangxi, Peoples R China
[3] Jiangxi Ctr Data & Applicat High Resolut Earth Ob, Nanchang 330095, Jiangxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Image color analysis; Spatial resolution; Kernel; Generators; Remote sensing; Generative adversarial networks; Training; Color harmony; deep neural networks; enblend; generative adversarial networks; LandSat; mosaicking; spatiotemporal fusion; RELATIVE RADIOMETRIC NORMALIZATION;
D O I
10.1109/LGRS.2021.3126261
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Remote sensing images of different moments or sensors can be stitched together to produce a new image under uniform geographic coordinate systems, where the overlapping areas were needed for color harmony. In this letter, a reference-based mosaicking method is proposed for images either with or without overlapping areas. The new method introduces a low-resolution image for spectral reference that spans all the mosaicking scope. A generative adversarial network is harnessed for color harmony, which transfers all the mosaicking images to the time of the reference image for further stitch with the graph cut and pyramid gradient methods. The proposed method is compared with three color harmony methods or tools by mosaicking the red, green, and blue bands of Landsat-8 images with MODIS as the reference. The digital evaluations demonstrate that the new method outweighs other methods regarding radiometric and spectral fidelity.
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页数:5
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