Classification And Recognition Of Ground-based Cloud Based On Image Features

The observation of clouds is a very fundamental and important aspect in the meteorological observations and has great directive significance on the observation of the weather system. The variation trend of clouds is also an important indicator to predict weather in the future. With the development of science and technology, the observation of clouds has developed from manual observation to intelligent observation. In intelligent observation area, there are mainly two kinds of observation techniques:the observation of satellite cloud imagery and the observation of ground-based cloud. Satellite cloud observations are more suitable to describe a wide range of clouds and their changes. On the other hand, ground-based cloud observations have great directive significance on the local cloud observations with the advantages of low cost, simpleness and easy to operate. And their applications play an increasingly important role in the current weather system observations. So far the work of ground-based cloud observations is usually accomplished by meteorologists. This work usually spends a lot of time and efforts, and also is affected by factors such as observation experience. So sometimes the results are not satisfactory. This paper is based on the research of the clouds intelligent recognition and classification in the past. An intelligent recognition and classification system of ground-based cloud is put forward in this paper. It overcomes the drawbacks of the manual observation and improves the observation efficiency. In the long term it will certainly become one part of automated meteorological observations. Research content and main work in this paper are as follows:(1) Materials of cloud classification at home and abroad are studied. The background knowledge and technical route are researched. It is found that the accurate rate is not high in the study of the classification of ground-based cloud. After obtaining the original experimental data, technologies such as sharpening and grayness are applied to these data. Then some texture feature extraction methods are researched and analyzed. There is more or less weakness in all of the texture feature extraction methods.(2) Texture feature extraction scheme for the ground-based nephogram is propounded. The choice of the Gabor filter texture feature extraction is made by considering of the research in the past. So the problem of texture feature extraction is solved. Then the texture feature data can be used in the classification of ground-based nephogram. The most widely used of BP neural network is chosen as a classifier after studying the neural network. Because there are some shortages of BP neural network, we discuss and select an optimization scheme. So the optimal BP neural network can be used in ground-based cloud classification.(3) Based on the learning of texture feature extraction and classification, we design and materialize a ground-based nephogram classifier using BP neural network. Five common types of clouds such as Cu cong, Nimbostratus and Ac lent are classified in this paper. Optimization and adjustment are made in accordance with the experimental results, and a second order classifier is created. Experimental results show that the second order classifier achieves good results.