A local features-based approach to all-sky image prediction


Estimation of cloud motion from all-sky image sequences is a very big challenge in the meteorological area that needs extensively study. In this paper, we propose a novel native options-based method to track clouds and to forecast short-term cloudiness up to fifteen minutes ahead using all-sky image sequences. This method allows us to estimate the cloud displacement furthermore as the change of size scale with high accuracy and robustness, leading to a vital improvement within the precision of cloud image prediction. The proposed Short-term All-sky Image Prediction System (SAIPS) includes 3 key steps: detection of clouds based on clustering, matching of the clouds from captured sequences, and analysis of cloud tracks to predict their locations. The effectiveness of the proposed system is verified using a database of captured pictures of real clouds. Experimental results demonstrate a better performance of the proposed approach compared to alternative algorithms.

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