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%.
Highlights
With the development and popularization of smartphones in recent years, the demand for diverse meteorological information by the public has continuously increased
The ability to use the mobile terminal to automatically recognize the ground-based cloud image at any time helps improve the automation of meteorological observations, and provides diverse meteorological information to general public and enhances the society's popularity of meteorological knowledge
Based on the Android platform, this paper presents an approach that uses four typical clouds as the recognition target
Summary
With the development and popularization of smartphones in recent years, the demand for diverse meteorological information by the public has continuously increased. The ability to use the mobile terminal to automatically recognize the ground-based cloud image at any time helps improve the automation of meteorological observations, and provides diverse meteorological information to general public and enhances the society's popularity of meteorological knowledge. Lu et al [Lu and Li (2015)] used textural feature training selective neural network to achieve cloud shape recognition. Network with the full sky cloud image He et al [He and Dantong (2018)] applied clustering analysis to satellite image segmentation and proposed a cloud-based thunderstorm cloud recognition method. Most of the above methods make use of the global characteristics of the image, local features have hardly been used to develop ground-based cloud image recognition on mobile terminals.
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