Relative Radiation Correction Based on CycleGAN for Visual Perception Improvement in High-Resolution Remote Sensing Images

doi:10.1109/ACCESS.2021.3101110

	Relative Radiation Correction Based on CycleGAN for Visual Perception Improvement in High-Resolution Remote Sensing Images
	Yu, Xiao 1,2; Fan, Junfu 1; Zhang, Mengzhen 1; Liu, Qingyun 1; Li, Yi 1; Zhang, Dafu 1; Zhou, Yuke 3
刊名	IEEE ACCESS
	2021
卷号	9 页码:106627-106640
关键词	Remote sensing Radiometry Visual perception Generative adversarial networks Visualization Indexes Image color analysis GAN image similarity seasonal transform visual perception distance
ISSN号	2169-3536
DOI	10.1109/ACCESS.2021.3101110
通讯作者	Fan, Junfu(fanjf@sdut.edu.cn)
英文摘要	The differences between the imaging environments of sensors lead to great differences in remote sensing images of the same area in different seasons. Relative radiation correction has high practical value as the main method to reduce such differences. However, the differences in vegetation radiation caused by seasonal changes are difficult to correct by traditional radiation correction methods. The corrected results also have difficulty achieving better results at the level of human eye visual perception. Moreover, the traditional measurement of the relative radiation correction result image quality index is not consistent with the human eye visual perception effect. To address the above two problems, this paper performs seasonal relative radiation correction on high-resolution remote sensing images by CycleGAN based on a convolutional neural network, including two transformations: 1) the transformation of remote sensing images from autumn-winter to spring-summer and 2) the transformation of remote sensing images from spring-summer to autumn-winter. The similarity between the relative radiation-corrected image and the reference image is measured by the convolutional neural network model with the ability to discriminate distances. The results show that the visual effect of this method is significantly better than that of other relative radiation correction methods, and the visual perception distance is consistent with the human eye visual perception judgment. The changed area still retains its original feature characteristics. The visual perception distance of the conversion from autumn-winter to spring-summer images is improved by 9% compared with other state-of-the-art methods. The visual perception distance of spring-summer images to autumn-winter images is improved by 3%. We expect that the method in this paper can be used for preprocessing to improve the accuracy of algorithms for remote sensing image classification, image change detection, etc.
资助项目	National Key Research and Development Program of China[2017YFB0503500] ; National Key Research and Development Program of China[2018YFB0505301] ; Shandong Provincial Natural Science Foundation[ZR2020MD015] ; Shandong Provincial Natural Science Foundation[ZR2020MD018] ; Major Science and Technology Innovation Project of Shandong Province[2019JZZY020103] ; Young Teacher Development Support Program of Shandong University of Technology[4072-115016]
WOS关键词	QUALITY ASSESSMENT ; RADIOMETRIC NORMALIZATION ; PERFORMANCE ; INFORMATION
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000681074500001
资助机构	National Key Research and Development Program of China ; Shandong Provincial Natural Science Foundation ; Major Science and Technology Innovation Project of Shandong Province ; Young Teacher Development Support Program of Shandong University of Technology
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/164735]
专题	中国科学院地理科学与资源研究所
通讯作者	Fan, Junfu
作者单位	1.Shandong Univ Technol, Sch Civil & Architectural Engn, Zibo 255000, Shandong, Peoples R China 2.XAG Co Ltd, Guangzhou 510663, Guangdong, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Ecol Observing Network & Modeling Lab, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Yu, Xiao,Fan, Junfu,Zhang, Mengzhen,et al. Relative Radiation Correction Based on CycleGAN for Visual Perception Improvement in High-Resolution Remote Sensing Images[J]. IEEE ACCESS,2021,9:106627-106640.
APA	Yu, Xiao.,Fan, Junfu.,Zhang, Mengzhen.,Liu, Qingyun.,Li, Yi.,...&Zhou, Yuke.(2021).Relative Radiation Correction Based on CycleGAN for Visual Perception Improvement in High-Resolution Remote Sensing Images.IEEE ACCESS,9,106627-106640.
MLA	Yu, Xiao,et al."Relative Radiation Correction Based on CycleGAN for Visual Perception Improvement in High-Resolution Remote Sensing Images".IEEE ACCESS 9(2021):106627-106640.