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Model-Based Segmentation of Cloud Structures In Satellite Image Sequences

Hussein Yahia 1 Jean-Paul Berroir 1 Gilles Mazars
1 AIR - Image Processing and Dynamic and Satellite Data
Inria Paris-Rocquencourt
Abstract : Meteorological image sequences acquired with remote sensing devices contain huge amounts of available data featuring the temporal evolution of highly deformable and complex structures. In this study is presented a robust and effective model-based segmentation procedure for approximating highly deformable cloud structures. The whole image sequence may be viewed as a 3D data set, but the classical techniques for performing 3D segmentation are not meaningful here, because cloud structures are not shapes in the classical sense. The model-based segmentation presented here uses level-sets controlled by particle systems. A specific energy formulation is introduced, permitting the use of robust conjugate gradient techniques. The model is validated on a real meteorological image sequence. We outline the generalizations of the this particle system to perform image analysis tasks on truly 3D images.
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https://hal.inria.fr/inria-00423792
Contributor : H. Yahia <>
Submitted on : Monday, October 12, 2009 - 5:51:38 PM
Last modification on : Wednesday, May 19, 2021 - 10:04:01 AM

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Hussein Yahia, Jean-Paul Berroir, Gilles Mazars. Model-Based Segmentation of Cloud Structures In Satellite Image Sequences. ICCV - Proceedings of the IEEE International Conference on Computer Vision, Jan 1998, Mumbai, India. pp.77-85. ⟨inria-00423792⟩

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