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Sparse analysis for mesoscale convective systems tracking

Abstract : In this paper, we study the tracking of de-formable shapes in sequences of images. Our target application is the tracking of clouds in satellite image. We propose to use a recent state-of-the-art method for off-the-grid sparse analysis to describe clouds in image as mixtures of 2D atoms. Then, we introduce an algorithm to handle the tracking with its specificities: apparition or disappearance of objects, merging, and splitting. This method provides similar numerical outputs as the recent state-of-the-art alternatives, while being more flexible, and providing additional information on, e.g., cloud surface brightness.
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Submitted on : Wednesday, September 16, 2020 - 3:38:29 PM
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Jean-Baptiste Courbot, Vincent Duval, Bernard Legras. Sparse analysis for mesoscale convective systems tracking. Signal Processing: Image Communication, Elsevier, 2020, 85, pp.115854. ⟨10.1016/j.image.2020.115854⟩. ⟨hal-02010436v2⟩



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