The Open Cybernetics & Systemics Journal

2015, 9 : 2877-2885
Published online 2015 November 02. DOI: 10.2174/1874110X01509012877
Publisher ID: TOCSJ-9-2877

Ground-based Vision Cloud Image Classification based on Extreme Learning Machine

Zhengping Wu , Xian Xu , Min Xia , Meifang Ma and Lin Li
Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China.

ABSTRACT

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

Keywords:

Cloud classification, Texture features, Color features, Sift features, Extreme learning machine.