Cloud discrimination using K Nearest Neighbors classifier: Application to dataset generated by Sétif RADAR (Algeria) and MSG-SEVIRI satellite images

Efficient classification depends strongly on the quality of the dataset used in experiments. In this paper, we generated a dataset consists of six spectral features extracted from the MSG-SEVIRI satellite images. Each feature represents the brightness temperature of the corresponding pixel. We are based on meteorological radar images acquired in Sétif region (Algeria) to assign a class to each feature vector, where we take account of the spatial and spectral resolution difference between radar and satellite images. We are interested to the identification of raining clouds, non-raining clouds and absence of clouds. The application of K Nearest Neighbors (KNN) classifier to the dataset generated performs very well. Using Euclidean metric for classifications, the overall accuracy is 99.46% and the Kappa coefficient attaint 99.13%. In order to validate the results obtained experimentally we have performed an in situ validation using eight ground measurements over the north Algeria. By computing different evaluation measure parameters, experimental results demonstrate the efficiency of the proposed methodology in discriminating between raining and non-raining clouds.