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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 931-932Classifiers for Ground-Based Cloud Images Using...

Classifiers for Ground-Based Cloud Images Using Texture Features

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Abstract:

The classification of ground-based cloud images has received more attention recently. The result of this work applies to the analysis of climate change; a correct classification is, therefore, important. In this paper, we used 18 texture features to distinguish 7 sky conditions. The important parameters of two classifiers are fine-tuned in the experiment, namely, k-nearest neighbor (k-NN) and artificial neural network (ANN). The performances of the two classifications were compared. Advantages and limitations of both classifiers were discussed. Our result revealed that the k-NN model performed at 72.99% accuracy while the ANN model has higher performance at 86.93% accuracy. We showed that our result is better than previous studies. Finally, seven most effective texture features are recommended to be used in the field of cloud type classification.

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Periodical:

Advanced Materials Research (Volumes 931-932)

Pages:

1392-1396

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.931-932.1392

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Online since:

May 2014

Authors:

Thitinan Kliangsuwan, Apichat Heednacram*

Keywords:

Cloud Classification, Ground-Based Images, Image Processing

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] W. C. K. Anuroj, Case Study of Stress from Thai Flood 2012, in School Age Student, Pathumthani Province, Thailand, R. Thai Airf. Med. Gaz., 58(1) (2012) 34–38.

[2] G. Guangmeng, and Y. Jie, Three Attempts of Earthquake Prediction with Satellite Cloud Images, Nat Hazards Earth Syst Sci, 13(1) (2013) 91–95.

DOI: 10.5194/nhess-13-91-2013

[3] M. Singh, and M. Glennen, Automated Ground-Based Cloud Recognition, Pattern Anal. Appl., 8(3) (2005) 258–271.

DOI: 10.1007/s10044-005-0007-5

[4] J. Calbó, and J. -A. González, Empirical Studies of Cloud Effects on UV Radiation: A Review, Rev. Geophys., 43(2) (2005).

DOI: 10.1029/2004rg000155

[5] J. T. Houghton, Y. Ding, D. J. Griggs, M. Noguer, P. J. van der LINDEN, X. Dai, K. Maskell, and C. A. Johnson, Climate Change 2001: The Scientific Basis, Cambridge Uni. Press, (2001).

DOI: 10.1002/qj.200212858119

[6] J. A. Parikh, Automatic Cloud Classification and Segmentation, Ph.D. dissertation, University of Maryland at College Park, College Park, MD, USA, (1977).

[7] A. Heinle, A. Macke, and A. Srivastav, Automatic Cloud Classification of Whole Sky Images, Atmospheric Meas. Tech. Discuss., 3(1) (2010) 269–299.

DOI: 10.5194/amt-3-557-2010

[8] J. Calbo, and J. Sabburg, Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition, J. Atmospheric Ocean. Technol., 25(1) (2008) 3–14.

DOI: 10.1175/2007jtecha959.1

[9] S. Liu, C. Wang, B. Xiao, Z. Zhang, and Y. Shao, Salient Local Binary Pattern for Ground-Based Cloud Classification, Acta Meteorol. Sin., 27 (2013) 211–220.

DOI: 10.1007/s13351-013-0206-8

[10] C. N. Long, J. M. Sabburg, J. Calbó, and D. Pages, Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images, JTECH, 23(5) (2006) 633–652.

DOI: 10.1175/jtech1875.1

[11] K. B. Widener, and C. N. Long, All Sky Imager, U.S. Patent Application 10/377, 042. (2003).

[12] M. P. Souza-Echer, E. B. Pereira, L. S. Bins, and M. A. R. Andrade, A Simple Method for the Assessment of the Cloud Cover State in High-Latitude Regions by a Ground-Based Digital Camera, J. Atmospheric Ocean. Technol., 23(3) (2006) 437–447.

DOI: 10.1175/jtech1833.1

[13] R. M. Haralick, K. Shanmugam, and I. H. Dinstein, Textural Features for Image Classification, IEEE Trans. on Syst. Man Cybern., (6) (1973) 610–621.

DOI: 10.1109/tsmc.1973.4309314

[14] O. Veksler, Nonparametric Density Estimation Nearest Neighbors, KNN [Online], Available: http: /www. cs. haifa. ac. il/~rita/ml_course/lectures/KNN. pdf (2013).

[15] I. H. Witten, and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, second ed., Morgan Kaufmann, San Francisco, (2005).

DOI: 10.1186/1475-925x-5-51

[16] I. Bonet, A. Rodríguez, R. Grau, M. M. García, Y. Saez, and A. Nowé, Comparing Distance Measures with Visual Methods, MICAI, AAI, Springer, California, 2008, p.90–99.

DOI: 10.1007/978-3-540-88636-5_8

[17] B. Karlik, and A. V. Olgac, Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks, IJAE, 1(4) (2011) 111–122.

[18] A. Kasapis, MLPs and Pose, Expression Classification, in Proc. of UNiS Report, (2003) 1-87.