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Cloud Detection for FY Meteorology Satellite Based on Ensemble Thresholds and Random Forests Approach

1
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1086 Xuyuan Avenue, Shenzhen 518055, China
2
College of Information Science & Technology, Chengdu University of Technology, 1 Dongshan Avenue Erxian Bridge, Chengdu 610059, China
3
State Key Laboratory of Space-Ground Integrated Information Technology, Space Star Technology CO., Ltd., 61rd Yard, Zhichun Road, Haidian District, Beijing 100086, China
4
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
5
University of Chinese Academy of Science, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(1), 44; https://doi.org/10.3390/rs11010044
Received: 20 November 2018 / Revised: 20 December 2018 / Accepted: 20 December 2018 / Published: 28 December 2018
(This article belongs to the Section Atmosphere Remote Sensing)
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PDF [10323 KB, uploaded 29 December 2018]
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Abstract

Cloud detection is the first step for the practical processing of meteorology satellite images, and also determines the accuracy of subsequent applications. For Chinese FY serial satellite, the National Meteorological Satellite Center (NSMC) officially provides the cloud detection products. In practical applications, there still are some misdetection regions. Therefore, this paper proposes a cloud detection method trying to improve NSMC’s products based on ensemble threshold and random forest. The binarization is firstly performed using ten threshold methods of the first infrared band and visible channel of the image, and the binarized images are obtained by the voting strategy. Secondly, the binarized images of the two channels are combined to form an ensemble threshold image. Then the middle part of the ensemble threshold image and the upper and lower margins of NSMC’s cloud detection result are used as the sample collection source data for the random forest. Training samples rely only on source image data at one moment, and then the trained random forest model is applied to images of other times to obtain the final cloud detection results. This method performs well on FY-2G images and can effectively detect incorrect areas of the cloud detection products of the NSMC. The accuracy of the algorithm is evaluated by manually labeled ground truth using different methods and objective evaluation indices including Probability of Detection (POD), False Alarm Rate (FAR), Critical Success Index (CSI) and the average and standard deviation of all indices. The accuracy results show that the proposed method performs better than the other methods with less incorrect detection regions. Though the proposed approach is simple enough, it is a useful attempt to improve the cloud detection result, and there is plenty of room for further improvement. View Full-Text
Keywords: ensemble threshold; random forest; FY meteorology satellite; cloud detection ensemble threshold; random forest; FY meteorology satellite; cloud detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Fu, H.; Shen, Y.; Liu, J.; He, G.; Chen, J.; Liu, P.; Qian, J.; Li, J. Cloud Detection for FY Meteorology Satellite Based on Ensemble Thresholds and Random Forests Approach. Remote Sens. 2019, 11, 44.

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