基于sar -光学数据融合和基于图的特征聚合网络的云去除

Remote. Sens. Pub Date : 2022-07-13 DOI:10.3390/rs14143374
Shanjing Chen, Wenjuan Zhang, Z. Li, Yuxi Wang, Bing-qing Zhang
{"title":"基于sar -光学数据融合和基于图的特征聚合网络的云去除","authors":"Shanjing Chen, Wenjuan Zhang, Z. Li, Yuxi Wang, Bing-qing Zhang","doi":"10.3390/rs14143374","DOIUrl":null,"url":null,"abstract":"In observations of Earth, the existence of clouds affects the quality and usability of optical remote sensing images in practical applications. Many cloud removal methods have been proposed to solve this issue. Among these methods, synthetic aperture radar (SAR)-based methods have more potential than others because SAR imaging is hardly affected by clouds, and can reflect ground information differences and changes. While SAR images used as auxiliary information for cloud removal may be blurred and noisy, the similar non-local information of spectral and electromagnetic features cannot be effectively utilized by traditional cloud removal methods. To overcome these weaknesses, we propose a novel cloud removal method using SAR-optical data fusion and a graph-based feature aggregation network (G-FAN). First, cloudy optical images and contemporary SAR images are concatenated and transformed into hyper-feature maps by pre-convolution. Second, the hyper-feature maps are inputted into the G-FAN to reconstruct the missing data of the cloud-covered area by aggregating the electromagnetic backscattering information of the SAR image, and the spectral information of neighborhood and non-neighborhood pixels in the optical image. Finally, post-convolution and a long skip connection are adopted to reconstruct the final predicted cloud-free images. Both the qualitative and quantitative experimental results from the simulated data and real data experiments show that our proposed method outperforms traditional deep learning methods for cloud removal.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":"130 1","pages":"3374"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Cloud Removal with SAR-Optical Data Fusion and Graph-Based Feature Aggregation Network\",\"authors\":\"Shanjing Chen, Wenjuan Zhang, Z. Li, Yuxi Wang, Bing-qing Zhang\",\"doi\":\"10.3390/rs14143374\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In observations of Earth, the existence of clouds affects the quality and usability of optical remote sensing images in practical applications. Many cloud removal methods have been proposed to solve this issue. Among these methods, synthetic aperture radar (SAR)-based methods have more potential than others because SAR imaging is hardly affected by clouds, and can reflect ground information differences and changes. While SAR images used as auxiliary information for cloud removal may be blurred and noisy, the similar non-local information of spectral and electromagnetic features cannot be effectively utilized by traditional cloud removal methods. To overcome these weaknesses, we propose a novel cloud removal method using SAR-optical data fusion and a graph-based feature aggregation network (G-FAN). First, cloudy optical images and contemporary SAR images are concatenated and transformed into hyper-feature maps by pre-convolution. Second, the hyper-feature maps are inputted into the G-FAN to reconstruct the missing data of the cloud-covered area by aggregating the electromagnetic backscattering information of the SAR image, and the spectral information of neighborhood and non-neighborhood pixels in the optical image. Finally, post-convolution and a long skip connection are adopted to reconstruct the final predicted cloud-free images. Both the qualitative and quantitative experimental results from the simulated data and real data experiments show that our proposed method outperforms traditional deep learning methods for cloud removal.\",\"PeriodicalId\":20944,\"journal\":{\"name\":\"Remote. Sens.\",\"volume\":\"130 1\",\"pages\":\"3374\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote. Sens.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/rs14143374\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote. Sens.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/rs14143374","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

在对地观测中,云的存在影响了实际应用中光学遥感图像的质量和可用性。为了解决这个问题,已经提出了许多清除云的方法。其中,基于合成孔径雷达(SAR)的方法由于其成像不受云层影响,能够反映地面信息的差异和变化,具有较大的潜力。作为辅助云去噪信息的SAR图像存在模糊和噪声问题,而传统的去云方法无法有效利用SAR图像中相似的非局部光谱和电磁特征信息。为了克服这些缺点,我们提出了一种新的基于sar光学数据融合和基于图的特征聚合网络(G-FAN)的云去除方法。首先,将浑浊光学图像与当代SAR图像进行拼接,并通过预卷积转换成超特征地图。其次,将超特征地图输入到G-FAN中,通过聚合SAR图像的电磁后向散射信息以及光学图像中邻域和非邻域像元的光谱信息,重建被云层覆盖区域的缺失数据;最后,采用后卷积和长跳跃连接对最终预测的无云图像进行重建。模拟数据和真实数据的定性和定量实验结果表明,本文提出的方法在云去除方面优于传统的深度学习方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cloud Removal with SAR-Optical Data Fusion and Graph-Based Feature Aggregation Network
In observations of Earth, the existence of clouds affects the quality and usability of optical remote sensing images in practical applications. Many cloud removal methods have been proposed to solve this issue. Among these methods, synthetic aperture radar (SAR)-based methods have more potential than others because SAR imaging is hardly affected by clouds, and can reflect ground information differences and changes. While SAR images used as auxiliary information for cloud removal may be blurred and noisy, the similar non-local information of spectral and electromagnetic features cannot be effectively utilized by traditional cloud removal methods. To overcome these weaknesses, we propose a novel cloud removal method using SAR-optical data fusion and a graph-based feature aggregation network (G-FAN). First, cloudy optical images and contemporary SAR images are concatenated and transformed into hyper-feature maps by pre-convolution. Second, the hyper-feature maps are inputted into the G-FAN to reconstruct the missing data of the cloud-covered area by aggregating the electromagnetic backscattering information of the SAR image, and the spectral information of neighborhood and non-neighborhood pixels in the optical image. Finally, post-convolution and a long skip connection are adopted to reconstruct the final predicted cloud-free images. Both the qualitative and quantitative experimental results from the simulated data and real data experiments show that our proposed method outperforms traditional deep learning methods for cloud removal.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信