Abstract
In this study, the degree of grayness of images of various types of cloud, collected from the Kunming Province (China) area, was statistically analyzed as part of a new weather recognition method to recognize weather patterns more accurately. The results reveal that the differences of the results of gray degree-based factor analysis vary remarkably with weather conditions. The image factor is the main factor in recognition, and the statistical factor is the reference factor. The recognition accurate level can be improved by up to 95.3% using the proposed approach. The proposed gray degree-based method outperforms wild line spread function and outdoor images support vector machine methods. The gray-scale method is easier to implement, timely, reliable, and accurate.
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Notes
The data are collected under a commissioned research project from Chinese Military; therefore, the dataset is not publicly available. However, it can be made available to other researchers in the field for benchmarking research, please contact the corresponding authors.
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Acknowledgements
This work was supported by the Startup Foundation for Doctors (No. PXY-BSQD-2014001), Educational Commission of Henan Province of China (No. 15A530010), to which the authors are greatly obliged, and Oriental Institute of Technology, Taiwan (No. 105-7-11-106).
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Fan, GF., Peng, LL., Hong, WC. et al. Applications of the Gray Degree-Based Factor Analysis on Cloud Image to Improve the Accuracy of Weather Recognition. Iran J Sci Technol Trans Sci 42, 2117–2129 (2018). https://doi.org/10.1007/s40995-017-0449-9
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DOI: https://doi.org/10.1007/s40995-017-0449-9