Abstract

In this paper, we present a novel DFT- and wavelet-based estimation scheme for hyperspectral imagery. Optimal hyperspectral image estimation relies on the ability to decorrelate the signal in both space and channel at the cost of requiring second-order signal statistics. This statistical requirement is removed by the proposed estimator, which approximately decorrelates the signal in space using a 2-D discrete wavelet transform and in channel using a discrete Fourier transform. In addition to allowing extremely efficient estimation, the proposed estimator vastly improves visual quality and yields typical signal-to-noise ratio gains of over 14 dB.

Original languageEnglish (US)
Pages743-745
Number of pages3
StatePublished - Nov 24 2003
Event2003 IGARSS: Learning From Earth's Shapes and Colours - Toulouse, France
Duration: Jul 21 2003Jul 25 2003

Other

Other2003 IGARSS: Learning From Earth's Shapes and Colours
CountryFrance
CityToulouse
Period7/21/037/25/03

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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  • Cite this

    Atkinson, I., Kamalabadi, F., & Jones, D. L. (2003). Wavelet-Based Hyperspectral Image Estimation. 743-745. Paper presented at 2003 IGARSS: Learning From Earth's Shapes and Colours, Toulouse, France.