Cloud and Cloud Shadow Detection Based on Multiscale 3D-CNN for High Resolution Multispectral Imagery
文献类型：期刊论文
| 作者 | Chen, Yang1; Tang, Luliang1; Kan, Zihan1; Latif, Aamir2; Yang, Xiucheng3; Bilal, Muhammad4; Li, Qingquan1,5 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2020 |
| 卷号 | 8页码:16505-16516 |
| 关键词 | Cloud detection cloud shadow convolution neural networks multiscale 3D-CNN |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2020.2967590 |
| 通讯作者 | Tang, Luliang(tll@whu.edu.cn) |
| 英文摘要 | Cloud and cloud shadow detection is one of the most important tasks for optical remote sensing image preprocessing. It is not an easy task due to the variety and complexity of underlying surfaces, such as the low-albedo objects (water and mountain shadow) and the high-albedo objects (snow and ice). In this study, an end-to-end multiscale 3D-CNN method is proposed for cloud and cloud shadow detection in high resolution multispectral imagery. Specifically, a multiscale learning module is designed to extract cloud and cloud shadow contextual information of different levels. In order to make full use of band information, four band-combination images are inputted into the multiscale 3D-CNN. A joint spectral-spatial information of 3D-convolution layer is developed to fully explore the joint spatial-spectral correlations feature in the input data. Overall, in the experiments undertaken in this paper, the proposed method achieved a mean overall accuracy of 97.27 & x0025; for cloud detection, with a mean precision of 96.02 & x0025; and a mean recall of 95.86 & x0025;. For cloud shadow detection, the proposed method achieved a mean precision of 95.92 & x0025; and a mean recall of 92.86 & x0025;. Experimental results on two validation datasets (GF-1 WFV validation data and ZY-3 validation data) show that the proposed multiscale-3D-CNN method achieved good performance with limited spectral ranges. |
| WOS关键词 | AUTOMATED CLOUD ; NEURAL-NETWORKS ; DEEP |
| 资助项目 | National Key Research and Development Plan of China[2017YFB0503604] ; National Key Research and Development Plan of China[2016YFE0200400] ; National Natural Science Foundation of China[41971405] ; National Natural Science Foundation of China[41671442] ; National Natural Science Foundation of China[41571430] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000524752200001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Plan of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/133758]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Tang, Luliang |
| 作者单位 | 1.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China 2.Univ Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 10010, Peoples R China 3.Univ Strasbourg, ICube Lab, F-67000 Strasbourg, France 4.Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing 210044, Peoples R China 5.Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Yang,Tang, Luliang,Kan, Zihan,et al. Cloud and Cloud Shadow Detection Based on Multiscale 3D-CNN for High Resolution Multispectral Imagery[J]. IEEE ACCESS,2020,8:16505-16516. |
| APA | Chen, Yang.,Tang, Luliang.,Kan, Zihan.,Latif, Aamir.,Yang, Xiucheng.,...&Li, Qingquan.(2020).Cloud and Cloud Shadow Detection Based on Multiscale 3D-CNN for High Resolution Multispectral Imagery.IEEE ACCESS,8,16505-16516. |
| MLA | Chen, Yang,et al."Cloud and Cloud Shadow Detection Based on Multiscale 3D-CNN for High Resolution Multispectral Imagery".IEEE ACCESS 8(2020):16505-16516. |
入库方式： OAI收割
来源：地理科学与资源研究所
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