Open Access

ARTICLE

Ground-Based Cloud Recognition Based on Dense_SIFT Features

Zhizheng Zhang¹, Jing Feng^1,*, Jun Yan², Xiaolei Wang¹, Xiaocun Shu¹

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

*Corresponding Author: Jing Feng. Email: .

Journal of New Media 2019, 1(1), 1-9. https://doi.org/10.32604/jnm.2019.05937

Abstract

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%.

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Citations