Ground-based Visible Cloud Image Classification Method Based on KNN Algorithm

Zhu Biao; Yang Jun; Lü Weitao; Chen Liying; Ma Ying; Yao Wen; Zhang Yijun

Vol.23, NO.6, 2012

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2012 > 23(6): 721-728

Zhu Biao, Yang Jun, Lü Weitao, et al. Ground-based visible cloud image classification method based on KNN algorithm. J Appl Meteor Sci, 2012, 23(6): 721-728.

Citation:

Zhu Biao, Yang Jun, Lü Weitao, et al. Ground-based visible cloud image classification method based on KNN algorithm. J Appl Meteor Sci, 2012, 23(6): 721-728.

Zhu Biao, Yang Jun, Lü Weitao, et al. Ground-based visible cloud image classification method based on KNN algorithm. J Appl Meteor Sci, 2012, 23(6): 721-728.

Citation:

Zhu Biao, Yang Jun, Lü Weitao, et al. Ground-based visible cloud image classification method based on KNN algorithm. J Appl Meteor Sci, 2012, 23(6): 721-728.

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Ground-based Visible Cloud Image Classification Method Based on KNN Algorithm

1.
Chengdu University of Information Technology, Chengdu 610225
2.
Chinese Academy of Meteorological Sciences, Beijing 100081
3.
Meteorological Service Center of Hainan Province, Haikou 570203

Received Date: 2012-02-14
Rev Recd Date: 2012-08-03
Publish Date: 2012-12-31

Abstract

Abstract

Cloud plays an important role in the meteorological research, and it is one of the most important factors of earth's energy balance and hydrological cycle. In order to actualize the automatic ground-based observation of clouds, automatic classification of cloud image is a difficult problem.A cloud classification scheme which classifies the cloud images into cumulus, stratus and cirrus is discussed. The clear sky is considered as a separate category in the scheme. Three kinds of image features, texture, color and shape are analyzed. The texture features describe the local information of image by using gray information normally, which have the characteristics for translation invariance. The color features consider the color of the image and focus on description of the overall image information, which have the characteristics for translation, rotation and scale invariability. The shape features describe the outline or region feature of the specific objectives and focus on description of single target. By analyzing the cloud image features of four different sky conditions, extraction algorithms are introduced in details. Using gray-level co-occurrence matrix and Tamura texture, color moment, and moment invariants, 21 characteristic parameters are extracted. Because of its high performance in solving complex issues, simplicity of implementation and low computational complexity, the K-Nearest Neighbor (KNN) classification algorithm is selected to process 21 characteristic parameters. 8 different K values and different features combination are used to recognize the 4 types of sky conditions. Classification experiments are conducted using single feature, combination of each two features, and all of these features together. The 7 experimental results demonstrate that the new scheme is feasible. And using texture features, color features and shape features together can get better performance than using these features alone or any two of them combined. When the parameter K is set to 7 and all 21 characteristic parameters are considered, the identification accuracy of cumulus, stratus, cirrus and clear sky are 91.1%, 74.4%, 70.0% and 100.0%, respectively, with the average accuracy up to 83.9%.
- texture features;
- color features;
- shape features;
- KNN;
- cloud classification

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References(26)

Figures and Tables

Fig. 1 Statistical distribution of recognition rate when the texture features are used in conjunction with shape and shape features

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图 2 几种典型的误分类情况

(a) 积状云误判为层状云, (b) 层状云误判为积状云, (c) 卷云误判为积状云

Fig. 2 Typical misclassification

(a) cumulus are mistaken for stratus, (b) stratus are mistaken for cumulus, (c) cirrus are mistaken for cumulus

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Table 1 Types of cloud image

分类	积状云	层状云	卷云
高云	卷积云	卷层云	卷云
中云	高积云 (蔽光高积云除外)	高层云，蔽光高积云
低云	积云，积雨云，积云性层积云，堡状层积云，荚状层积云	层云，雨层云，透光层积云，蔽光层积云

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Table 2 Average values of characteristic parameters

特征量	积状云	层状云	卷云	晴空
Tamura粗糙度	0.8911	0.8697	0.8652	0.6877
Tamura对比度	0.3967	0.2079	0.2737	0.0941
Tamura方向度	0.4167	0.3057	0.3776	0.0398
GLCM对比度	0.1865	0.1535	0.1614	0.1471
GLCM相关	0.9774	0.9282	0.9617	0.7906
GLCM能量	0.3464	0.5426	0.4382	0.7296
GLCM局部平稳	0.9841	0.9869	0.9862	0.9930
GLCM熵	0.4183	0.5546	0.3698	0.2945
色调 (一阶分量)	0.5987	0.3735	0.6377	0.6628
饱和度 (一阶分量)	0.3237	0.1717	0.4548	0.6817
亮度 (一阶分量)	0.6561	0.7358	0.6617	0.6565
色调 (二阶分量)	0.2154	0.2841	0.0985	0.0070
饱和度 (二阶分量)	0.4041	0.1201	0.3183	0.0996
亮度 (二阶分量)	0.4429	0.2905	0.2790	0.1488
不变矩1	0.6811	0.6798	0.6990	0.7125
不变矩2	0.2807	0.3230	0.3148	0.3017
不变矩3	0.3445	0.2352	0.3579	0.2435
不变矩4	0.2943	0.2036	0.3063	0.2067
不变矩5	0.3256	0.2177	0.3106	0.2135
不变矩6	0.0097	0.0153	0.0096	0.0140
不变矩7	0.2382	0.1528	0.2355	0.1740

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Table 3 Confusion matrix using texture features alone when K is set to 5

天空类型	积状云	层状云	卷云	晴空
积状云	57.8%(52)	21.1%(19)	21.1%(19)	0.0%(0)
层状云	7.8%(7)	72.2%(65)	13.3%(12)	6.7%(6)
卷云	25.6%(23)	22.2%(20)	51.1%(46)	1.1%(1)
晴空	1.1%(1)	10.0%(9)	1.1%(1)	87.8%(79)
注：括号内为相应的样本数。

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Table 4 Confusion matrix using color features alone when K is set to 11

天空类型	积状云	层状云	卷云	晴空
积状云	82.2%(74)	5.6%(5)	12.2%(11)	0.0%(0)
层状云	11.1%(10)	77.8%(70)	10.0%(9)	1.1%(1)
卷云	24.4%(22)	8.9%(8)	58.9%(53)	7.8%(7)
晴空	0.0%(0)	0.0%(0)	1.1%(1)	98.9%(89)
注：括号内为相应的样本数。

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Table 5 Confusion matrix using shape features alone when K is set to 1

天空类型	积状云	层状云	卷云	晴空
积状云	32.2%(29)	28.9%(26)	31.1%(28)	7.8%(7)
层状云	20.0%(18)	41.1%(37)	18.9%(17)	20.0%(18)
卷云	26.7%(24)	21.1%(19)	35.6%(32)	16.7%(15)
晴空	3.3%(3)	15.6%(14)	13.3%(12)	67.8%(61)
注：括号内为相应的样本数。

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Table 6 Confusion matrix when the texture features are used in conjunction with color features and K is set to 51

天空类型	积状云	层状云	卷云	晴空
积状云	86.7%(78)	6.7%(6)	6.7%(6)	0.0%(0)
层状云	11.1%(10)	76.7%(69)	11.1%(10)	1.1%(1)
卷云	23.3%(21)	6.7%(6)	68.9%(62)	1.1%(1)
晴空	0.0%(0)	0.0%(0)	1.0%(1)	98.9%(89)
注：括号内为相应的样本数。

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Table 7 Confusion matrix when the texture features are used in conjunction with shape features and K is set to 31

天空类型	积状云	层状云	卷云	晴空
积状云	61.1%(55)	21.1%(19)	17.8%(16)	0.0%(0)
层状云	8.9%(8)	65.6%(59)	17.8%(16)	7.8%(7)
卷云	22.2%(20)	18.9%(17)	55.6%(50)	3.3%(3)
晴空	0.0%(0)	7.8%(7)	2.2%(2)	90.0%(81)
注：括号内为相应的样本数。

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Table 8 Confusion matrix when the color features are used in conjunction with shape features and K is set to 31

天空类型	积状云	层状云	卷云	晴空
积状云	88.9%(80)	4.4%(4)	6.7%(6)	0.0%(0)
层状云	12.2%(11)	76.7%(69)	10.0%(9)	1.1%(1)
卷云	16.7%(15)	11.1%(10)	63.3%(57)	8.9%(7)
晴空	0.0%(0)	0.0%(0)	6.6%(6)	93.4%(84)
注：括号内为相应的样本数。

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Table 9 Confusion matrix when the texture features are used in conjunction with color and shape features and K is set to 7

天空类型	积状云	层状云	卷云	晴空
积状云	91.1%(82)	5.6%(5)	3.3%(3)	0.0%(0)
层状云	12.2%(11)	74.4%(67)	11.1%(10)	2.2%(2)
卷云	20.0%(18)	7.8%(7)	70.0%(63)	2.2%(2)
晴空	0.0%(0)	0.0%(0)	0.0%(0)	100.0%(90)
注：括号内为相应的样本数。

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