An Improved Cloud Detection Method for GF-4 Imagery
文献类型：期刊论文
| 作者 | Lu, Ming2; Li, Feng2; Zhan, Bangcheng2,3; Li, He4; Yang, Xue2; Lu, Xiaotian2; Xiao, Huachao1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2020-05-01 |
| 卷号 | 12期号:9页码:23 |
| 关键词 | cloud detection GF-4 real-time difference remote sensing |
| DOI | 10.3390/rs12091525 |
| 通讯作者 | Li, Feng(lifeng@qxslab.cn) |
| 英文摘要 | Clouds are significant barriers to the application of optical remote sensing images. Accurate cloud detection can help to remove contaminated pixels and improve image quality. Many cloud detection methods have been developed. However, traditional methods either rely heavily on thermal infrared bands or clear-sky images. When traditional cloud detection methods are used with Gaofen 4 (GF-4) imagery, it is very difficult to separate objects with similar spectra, such as ice, snow, and bright sand, from clouds. In this paper, we propose a new method, named Real-Time-Difference (RTD), to detect clouds using a pair of images obtained by the GF-4 satellite. The RTD method has four main steps: (1) data preprocessing, including transforming digital value (DN) to Top of Atmosphere (TOA) reflectance, and orthographic and geometric correction; (2) the computation of a series of cloud indexes for a single image to highlight clouds; (3) the calculation of the difference between a pair of real-time images in order to obtain moved clouds; and (4) confirming the clouds and background by analyzing their physical and dynamic features. The RTD method was validated in three sites located in the Hainan, Liaoning, and Xinjiang areas of China. The results were compared with those of a popular classifier, Support Vector Machine (SVM). The results showed that RTD outperformed SVM; for the Hainan, Liaoning, and Xinjiang areas, respectively, the overall accuracy of RTD reached 95.9%, 94.1%, and 93.9%, and its Kappa coefficient reached 0.92, 0.88, and 0.88. In the future, we expect RTD to be developed into an important means for the rapid detection of clouds that can be used on images from geostationary orbit satellites. |
| WOS关键词 | LANDSAT TIME-SERIES ; SHADOW DETECTION ; AUTOMATED CLOUD ; ALGORITHM ; SATELLITE ; GENERATION ; TRANSFORM ; WATER |
| 资助项目 | National Key Research and Development Projects[2016YFB0501301] ; National Natural Science Foundation of China[61773383] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000543394000172 |
| 出版者 | MDPI |
| 资助机构 | National Key Research and Development Projects ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/162398]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Feng |
| 作者单位 | 1.Acad Space Informat Syst, Xian 710100, Peoples R China 2.Qian Xuesen Lab Space Technol, Beijing 100094, Peoples R China 3.Henan Univ, Coll Comp & Informat Engn, Kaifeng 475001, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Ming,Li, Feng,Zhan, Bangcheng,et al. An Improved Cloud Detection Method for GF-4 Imagery[J]. REMOTE SENSING,2020,12(9):23. |
| APA | Lu, Ming.,Li, Feng.,Zhan, Bangcheng.,Li, He.,Yang, Xue.,...&Xiao, Huachao.(2020).An Improved Cloud Detection Method for GF-4 Imagery.REMOTE SENSING,12(9),23. |
| MLA | Lu, Ming,et al."An Improved Cloud Detection Method for GF-4 Imagery".REMOTE SENSING 12.9(2020):23. |
入库方式： OAI收割
来源：地理科学与资源研究所
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