A New Extended Linear Mixing Model to Address Spectral Variability

Abstract : Spectral variability is a phenomenon due, to a grand extend, to variations in the illumination and atmospheric conditions within a hyperspectral image, causing the spectral signature of a material to vary within a image. Data spectral fluctuation due to spectral variability compromises the linear mixing model (LMM) sum-to-one constraint, and is an important source of error in hyperspectral image analysis. Recently, spectral variability has raised more attention and some techniques have been proposed to address this issue, i.e. spectral bundles. Here, we propose the definition of an extended LMM (ELMM) to model spectral variability and we show that the use of spectral bundles models the ELMM implicitly. We also show that the constrained least squares (CLS) is an explicit modelling of the ELMM when the spectral variability is due to scaling effects. We give experimental validation that spectral bundles (and sparsity) and CLS are complementary techniques addressing spectral variability. We finally discuss on future research avenues to fully exploit the proposed ELMM.
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Communication dans un congrès
IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2014), Jun 2014, Lausanne, Switzerland. pp.n/c, 2014
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Contributeur : Miguel Angel Veganzones <>
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  • HAL Id : hal-01010424, version 1

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Miguel Angel Veganzones, Lucas Drumetz, Guillaume Tochon, Mauro Dalla Mura, Antonio Plaza, et al.. A New Extended Linear Mixing Model to Address Spectral Variability. IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2014), Jun 2014, Lausanne, Switzerland. pp.n/c, 2014. 〈hal-01010424〉

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