Foundation cloud classification method based on self-adaptive extreme learning machine

Abstract

The invention discloses a foundation cloud picture cloud classification method, and belongs to the technical field of image information processing and weather. The method comprises the following steps that (1) the textural feature, shape feature and color feature of a cloud picture are extracted to form a 21-dimensional feature vector; (2) normalization processing is carried out on each bit of the 21-dimensional feature vector; (3) a self-adaptive extreme learning machine model is built, and network training is carried out through a training sample; (4) the normalized 21-dimensional feature vector is adopted as the input of the self-adaptive extreme learning machine, and the varieties of clouds are adopted as output for cloud classification. The textural feature, shape feature and color feature of the cloud picture are comprehensively utilized, the self-adaptive extreme learning machine model based on k neighbors and the extreme learning machine is built, the foundation clouds are accurately classified, classification performance of the method is more accurate than that of an existing method, and the important application value is achieved.

Description

technical field [0001] The invention relates to the technical fields of image analysis and meteorology, in particular to a cloud classification method for ground-based cloud images. Background technique [0002] As the most obvious and common atmospheric state in the Earth's atmosphere, clouds have always attracted people's attention. On average, 1 / 3 to 1 / 2 of the earth is covered with clouds. Clouds are important players in weather processes. As the saying goes, "Look at the clouds to know the weather", certain weather phenomena are always associated with certain clouds. Before the creation of the discipline of meteorology, people in agricultural society began to pay close attention to changes in clouds because of the close relationship between weather and agricultural production. Based on our experience, we have summed up the relationship between some clouds and weather changes. It can be seen that the role of clouds in the study of weather phenomena and atmospheric con...

AI Technical Summary

Problems solved by technology

The traditional neural network adopts the gradient learning method (BP) of error feedback, which has the disadvantages of slow learning speed, too many iterations, and the solution is easy to fall into local minimum.

These drawbacks severely hamper the application of neural networks in cloud classification

In addition, the neural network lacks systematic modeling, so when the difference between the input data and the learning data is relatively large, it is prone to error amplification

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