NDLI: Based on K-Means Clustering and CNN Algorithm Research in Hail Cloud Determination

Content Provider	IEEE Xplore Digital Library
Author	Wang Xue Liao Feijia Xu Wenxia Guo Kun Li Guodong
Copyright Year	2015
Description	Author affiliation: Sch. of Appl. Math., Xinjiang Univ. of Finance & Econ., Urumqi, China (Wang Xue; Liao Feijia; Xu Wenxia; Guo Kun; Li Guodong)
Abstract	Hail is one of the main meteorological disasters in our country, also it is the hot current research. The forecasting of hail is mostly used in the data return from radar. Based on radar return image, using the statistics of the K-means clustering algorithm and cellular neural networks (CNN) algorithm, processing the cloud image, and get the difference between the inside and outside layer outline. Compare the hail cloud image with the no hail cloud image to get the variance, we can find the difference of internal and external contours Using the hail cloud images and the no hail cloud image of Shihezi in Xinjiang province verification, validation of the conclusion of this paper is effective. Show that combined with K means clustering algorithm and CNN contour extraction algorithm to get the difference of internal and external contours is a effective method of forecasting the hail.
Sponsorship	Hunan Inst. Ind.
Starting Page	232
Ending Page	235
File Size	619247
Page Count	4
File Format	PDF
e-ISBN	9781467371438
DOI	10.1109/ICMTMA.2015.64
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	CNN Image color analysis Clouds Image edge detection difference of internal and external contours Clustering algorithms Lab color space contour extraction contour Cellular neural networks Mathematical model K means clustering Meteorology
Content Type	Text
Resource Type	Article

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