Hyperspectral image analysis using multifractal attributes

Abstract : The increasing spatial resolution of hyperspectral remote sensors requires the development of new processing methods capable of combining both spectral and spatial information.In this article, we focus on the spatial component and propose the use of novel multifractal attributes, which extract spatial information in terms of the fluctuations of the local regularity of image amplitudes. The novelty of the proposed approach is twofold. First, unlike previous attempts, we study attributes that efficiently summarize multifractal information in a few parameters. Second, we make use of the most recent developments in multifractal analysis: wavelet leader multifractal formalism, the current theoretical and practical benchmark in multifractal analysis, and a novel Bayesian estimation procedure for one of the central multifractal parameters. Attributes provided by these state-of-the-art multifractal analysis procedures are studied on two sets of hyperspectral images. The experiments suggest that multifractal analysis can provide relevant spatial/textural attributes which can in turn be employed in tasks such as classification or segmentation.
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  • HAL Id : hal-01511880, version 1
  • OATAO : 17041

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Sébastien Combrexelle, Herwig Wendt, Jean-Yves Tourneret, Stephen Mclaughlin, Patrice Abry. Hyperspectral image analysis using multifractal attributes. 7th IEEE Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS 2015), Jun 2015, Tokyo, Japan. pp. 1-4. ⟨hal-01511880⟩

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