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Hypergraph-based Algorithm for Segmentation of Weather Satellite Imagery
Objective: Classification of cloud images through segmentation of automated satellite images to improvise the level of accuracy. Method Analysis: To classify cloud images the hyper graph model uses the idea of maximally bonded subsets that is endowed with integer valued metric are applied to receive the classifications. The widely used hyper graph model is Intensity Neighborhood Hyper graph (INHG) and representation model in this article is Intensity Interval Hyper graph (IIHG). Findings: The results obtained through this process is proved to be more accurate and the time complexity is O(n) in weather prediction. Similarly, the results received through IIHG, which also provides the same computational complexity where all the pixels to be processed with less time. Enhancement: The proposed methodology increases the accuracy level of prediction with less computation time and this work can be enhanced by including pattern recognition in automated processing.
Hyper Graph, INHG, IIHG, Satellite Imagery, Segmentation.
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