Atmospheric invariants for hyperspectral image correction

The degrading effect of the atmosphere on hyperspectral imagery has long been recognised as a major issue in applying techniques such as spectrally-matched filters to hyperspectral data. There are a number of algorithms available in the literature for the correction of hyperspectral data. However most of these approaches rely either on identifying objects within a scene (e.g. water whose spectral characteristics are known) or by measuring the relative effects of certain absorption features and using this to construct a model of the atmosphere which can then be used to correct the image. In the work presented here, we propose an alternative approach which makes use of the fact that the effective number of degrees of freedom in the atmosphere (transmission, path radiance and downwelling radiance with respect to wavelength) is often substantially less than the number of degrees of freedom in the spectra of interest. This allows the definition of a fixed set of invariant features (which may be linear or non-linear) from which reflectance spectra can be approximately reconstructed irrespective of the particular-atmosphere. The technique is demonstrated on a range of data across the visible to near infra-red, mid-wave and long-wave infra-red regions, where its performance is quantified.