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An AM-FM Based Image Segmentation: Detection of Clouds in MSG Images of Algeria


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DOI: https://doi.org/10.15866/irecos.v10i7.7107

Abstract


In this paper, amplitude and frequency modulation (AM-FM) data are extracted from satellite images and processed to facilitate meteorological observations. The approach used is mainly based on 2D-DESA (Discrete Energy Separation Algorithm) where the Teager Kaiser Energy Operator (TKEO) acts as data discriminator. Dominant energy parameters selected by this operator, are employed to get the AM-FM components and segment the images under study via K-means classifier. The AM-FM method so described has been tested on a set of multispectral satellite images collected by Meteosat 9 (MSG-2) over North Africa in November 2011. Applied to both visible and infrared Meteosat Second Generation (MSG) images, it yields a compact and coherent segmentation map of Northern Algeria, Mediterranean Sea and South Western Europe, where the object contours are satisfactorily reproduced and typical clouds, detected.
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Keywords


Image Segmentation; Brightness; Texture; Weather Satellite; Cloud Detection

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References


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