Segmentation of Extreme Ultraviolet Solar Images using a Multispectral Data Fusion Process

Abstract : — Accurate means of quantifying the respective contributions of different structures to the solar irradiance is now a key issue in Solar Physics, with implications to Sun-Earth relationships and space weather study. In this paper, we propose a three-step fusion scheme, that allows to aggregate (17.1 nm, 19.5 nm) data stemming from the solar EIT instrument onboard the SoHO mission, and that is flexible enough to allow the integration of other type of information. The method is based on both a spatially constrained possibilistic clustering algorithm and a context dependent fusion operator. It aggregates the complementary and redundant information coming from the input sources. The results obtained on a 9-year dataset are consistent with those found in the solar physics literature. Unlike previous algorithms used in solar physics, our method has the ability to add further heterogeneous sources and sensors (e.g. human knowledge, images in other bandpasses, ratio of images) to the process, in order to postpone the decision step (here the segmentation of structures of interest) until sufficient information is available.
Type de document :
Article dans une revue
2007 IEEE International Fuzzy Systems Conference, 2007, pp.1 - 6. 〈10.1109/FUZZY.2007.4295367〉
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https://hal-clermont-univ.archives-ouvertes.fr/hal-01622925
Contributeur : Vincent Barra <>
Soumis le : mardi 24 octobre 2017 - 18:31:02
Dernière modification le : jeudi 11 janvier 2018 - 06:28:14

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Vincent Barra, Véronique Delouille, Jean-François Hochedez. Segmentation of Extreme Ultraviolet Solar Images using a Multispectral Data Fusion Process. 2007 IEEE International Fuzzy Systems Conference, 2007, pp.1 - 6. 〈10.1109/FUZZY.2007.4295367〉. 〈hal-01622925〉

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