Ground-based Visible Cloud Image Classification Method Based on KNN Algorithm

Cloud plays an important role in the meteorological research, and it is one of the most important factors of earth's energy balance and hydrological cycle. In order to actualize the automatic ground-based observation of clouds, automatic classification of cloud image is a difficult problem.A cloud classification scheme which classifies the cloud images into cumulus, stratus and cirrus is discussed. The clear sky is considered as a separate category in the scheme. Three kinds of image features, texture, color and shape are analyzed. The texture features describe the local information of image by using gray information normally, which have the characteristics for translation invariance. The color features consider the color of the image and focus on description of the overall image information, which have the characteristics for translation, rotation and scale invariability. The shape features describe the outline or region feature of the specific objectives and focus on description of single target. By analyzing the cloud image features of four different sky conditions, extraction algorithms are introduced in details. Using gray-level co-occurrence matrix and Tamura texture, color moment, and moment invariants, 21 characteristic parameters are extracted. Because of its high performance in solving complex issues, simplicity of implementation and low computational complexity, the K-Nearest Neighbor (KNN) classification algorithm is selected to process 21 characteristic parameters. 8 different K values and different features combination are used to recognize the 4 types of sky conditions. Classification experiments are conducted using single feature, combination of each two features, and all of these features together. The 7 experimental results demonstrate that the new scheme is feasible. And using texture features, color features and shape features together can get better performance than using these features alone or any two of them combined. When the parameter K is set to 7 and all 21 characteristic parameters are considered, the identification accuracy of cumulus, stratus, cirrus and clear sky are 91.1%, 74.4%, 70.0% and 100.0%, respectively, with the average accuracy up to 83.9%.