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Ground-Based Cloud Recognition Based on Dense_SIFT Features

Zhizheng Zhang1, Jing Feng1,*, Jun Yan2, Xiaolei Wang1, Xiaocun Shu1

National University of Defense Technology, Nanjing, 21101, China.
University of Wollongong, Wollongong NSW, 2522, Australia.

*Corresponding Author: Jing Feng. Email: .

Journal of New Media 2019, 1(1), 1-9.


Clouds play an important role in modulating radiation processes and climate changes in the Earth's atmosphere. Currently, measurement of meteorological elements such as temperature, air pressure, humidity, and wind has been automated. However, the cloud's automatic identification technology is still not perfect. Thus, this paper presents an approach that extracts dense scale-invariant feature transform (Dense_SIFT) as the local features of four typical cloud images. The extracted cloud features are then clustered by K-means algorithm, and the bag-of-words (BoW) model is used to describe each ground-based cloud image. Finally, support vector machine (SVM) is used for classification and recognition. Based on this design, a nephogram recognition intelligent application is implemented. Experiments show that, compared with other classifiers, our approach has better performance and achieved a recognition rate of 88.1%.


Cite This Article

Z. Zhang, J. Feng, J. Yan, X. Wang and X. Shu, "Ground-based cloud recognition based on dense_sift features," Journal of New Media, vol. 1, no.1, pp. 1–9, 2019.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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