A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm
文献类型：期刊论文
| 作者 | Ping Bo1; Su Fenzhen2 ; Meng Yunshan3
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| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2020 |
| 卷号 | 13页码:1714-1727 |
| 关键词 | Cloud and cloud shadow detection fuzzy c-means algorithm multiple features multispectral sensors |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2020.2987844 |
| 通讯作者 | Ping Bo(pingbo@tju.edu.cn) |
| 英文摘要 | Cloud and cloud shadow detection is an important preprocess before using satellite images for different applications. It can be considered as a classification process, in which the objective pixels are partitioned into cloud/cloud shadow or non-cloud/non-cloud shadow classes. However, some cloud pixels, especially the thin cloud pixels, can be considered as a mixture of reflectances of clouds and land objects. In fuzzy clustering, the data points can belong to two or more clusters; hence, fuzzy clustering may better characterize the status of one given pixel belonging to clouds or non-clouds. The fuzzy c-means method (FCM), one typical fuzzy clustering method, was utilized in this study for cloud and cloud shadow detection. In addition, the "flood-fill" morphological transformation may misclassify some clear-sky areas surrounded by clouds as cloud shadows as a whole, so a modified cloud shadow index calculation was proposed. Moreover, a cloud and cloud shadow spatial matching strategy based on the projection direction and spatial coexistence was used to exclude some pseudo cloud shadows. Fewer predefined parameters and spectral bands are needed is one characteristic of the proposed method. In this study, 41 scenes including 27 Landsat ETM+ images in eight latitude zones and 14 Landsat OLI images comprising seven land cover types, including barren, forest, grass, shrubland, urban, water, and wetlands areas, with percentages of cloud cover from 4.99% to 97.63%, were utilized to confirm the validity of the FCM. The detected results demonstrate that the thick and thin clouds along with their associated cloud shadows can be precisely extracted by using the FCM. Compared with the function of mask (Fmask) method, the FCM has relatively lower producer agreement rates, but it misclassifies as clouds fewer clear-sky pixels; compared with the support vector machine (SVM) method, the FCM can achieve better cloud detection accuracy. The results demonstrate that the FCM can attain a better balance between cloud pixel detection and non-cloud pixel exclusion. |
| WOS关键词 | AUTOMATED CLOUD ; LANDSAT IMAGERY ; SNOW DETECTION ; MODIS |
| 资助项目 | Natural Science Foundation of Tianjin[18JCQNJC08900] ; State Key Laboratory of Resources and Environmental Information System |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000534051300001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Natural Science Foundation of Tianjin ; State Key Laboratory of Resources and Environmental Information System |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/159706]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ping Bo |
| 作者单位 | 1.Tianjin Univ, Inst Surface Earth Syst Sci, Tianjin 300072, Peoples R China 2.Univ Chinese Acad Sci, LREIS, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Natl Marine Data & Informat Serv, Tianjin 300171, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ping Bo,Su Fenzhen,Meng Yunshan. A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2020,13:1714-1727. |
| APA | Ping Bo,Su Fenzhen,&Meng Yunshan.(2020).A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,13,1714-1727. |
| MLA | Ping Bo,et al."A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 13(2020):1714-1727. |
入库方式： OAI收割
来源：地理科学与资源研究所
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