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	An improved cloud classification algorithm for china's fy-2c multi-channel images using artificial neural network
	Liu, Yu1,2; Xia, Jun1; Shi, Chun-Xiang3; Hong, Yang4
刊名	Sensors
	2009-07-01
卷号	9期号:7页码:5558-5579
关键词	Fy-2c Multi-channel satellite image Ann Cloud classification
ISSN号	1424-8220
DOI	10.3390/s90705558
通讯作者	Xia, jun(xiaj@igsnrr.ac.cn)
英文摘要	The crowning objective of this research was to identify a better cloud classification method to upgrade the current window-based clustering algorithm used operationally for china's first operational geostationary meteorological satellite fengyun-2c (fy-2c) data. first, the capabilities of six widely-used artificial neural network (ann) methods are analyzed, together with the comparison of two other methods: principal component analysis (pca) and a support vector machine (svm), using 2864 cloud samples manually collected by meteorologists in june, july, and august in 2007 from three fy-2c channel (ir1, 10.3-11.3 mu m; ir2, 11.5-12.5 mu m and wv 6.3-7.6 mu m) imagery. the result shows that: (1) ann approaches, in general, outperformed the pca and the svm given sufficient training samples and (2) among the six ann networks, higher cloud classification accuracy was obtained with the self-organizing map (som) and probabilistic neural network (pnn). second, to compare the ann methods to the present fy-2c operational algorithm, this study implemented som, one of the best ann network identified from this study, as an automated cloud classification system for the fy-2c multi-channel data. it shows that som method has improved the results greatly not only in pixel-level accuracy but also in cloud patch-level classification by more accurately identifying cloud types such as cumulonimbus, cirrus and clouds in high latitude. findings of this study suggest that the ann-based classifiers, in particular the som, can be potentially used as an improved automated cloud classification algorithm to upgrade the current window-based clustering method for the fy-2c operational products.
WOS关键词	PATTERN-RECOGNITION ; AVHRR IMAGERY ; SURFACE ; RETRIEVAL ; REGIONS ; SCHEME
WOS研究方向	Chemistry ; Electrochemistry ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation
语种	英语
出版者	MOLECULAR DIVERSITY PRESERVATION INTERNATIONAL-MDPI
WOS记录号	WOS:000268317000029
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2399153
专题	中国科学院大学
通讯作者	Xia, Jun
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Grad Sch, Beijing 100039, Peoples R China 3.Natl Satellite Meteorol Ctr, Beijing 100081, Peoples R China 4.Univ Oklahoma, Sch Civil Engn & Environm Sci, Norman, OK 73019 USA
推荐引用方式 GB/T 7714	Liu, Yu,Xia, Jun,Shi, Chun-Xiang,et al. An improved cloud classification algorithm for china's fy-2c multi-channel images using artificial neural network[J]. Sensors,2009,9(7):5558-5579.
APA	Liu, Yu,Xia, Jun,Shi, Chun-Xiang,&Hong, Yang.(2009).An improved cloud classification algorithm for china's fy-2c multi-channel images using artificial neural network.Sensors,9(7),5558-5579.
MLA	Liu, Yu,et al."An improved cloud classification algorithm for china's fy-2c multi-channel images using artificial neural network".Sensors 9.7(2009):5558-5579.

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