Ground-based Vision Cloud Image Classification based on ExtremeLearning Machine

Cloud radiation properties and distribution significantly affect the forecasting accuracy, climate monitoring effectiveness and global climate’s change. A simple method proposed to automatically recognize four different sky conditions (cirrus, cumulus, stratus and clear sky) by means of extracting some features from vision images and that be useful for training classifier. In this paper, texture features, color features and SIFT features are extracted and extreme learning machine are used to cloud-type classification under different experimental conditions. The experiment results show that the proposed approach using texture features, color features and SIFT features together get better performance than using these features alone or any two of them together, the accurate identification rate of cirrus, cumulus, stratus and clear sky are 87.67%, 90.75%, 74.50% and 93.63%, with an average of 86.64%. Under the same experiment condition, the proposed method outperform artificial neutral network (ANN), k-nearest neighbor (KNN) and support vector machine (SVM).

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