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Resolution Enhancement of Hyperion Hyperspectral Data using Ikonos Multispectral Data

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Conference paper

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We have developed a new and innovative technique for combining a high-spatial-resolution multispectral image with a lower-spatial-resolution hyperspectral image. The approach, called CRISP, compares the spectral information present in the multispectral image to the spectral content in the hyperspectral image and derives a set of equations to approximately transform the multispectral image into a synthetic hyperspectral image. This synthetic hyperspectral image is then recombined with the original low-spatial-resolution hyperspectral image to produce a sharpened product. The result is a product that has the spectral properties of the hyperspectral image at a spatial resolution approaching that of the multispectral image. To test the accuracy of the CRISP method, we applied the method to synthetic data generated from hyperspectral images acquired with an airborne sensor. These high-spatial-resolution images were used to generate both a lower-spatial-resolution hyperspectral data set and a four-band multispectral data set. With this method, it is possible to compare the output of the CRISP process to the truth data the original scene. In all of these controlled tests, the CRISP product showed both good spectral and visual fidelity, with an RMS error less than one percent when compared to the truth image. We then applied the method to real world imagery collected by the Hyperion sensor on EO-1 as part of the Hurricane Katrina support effort. In addition to multiple Hyperion data sets, both Ikonos and QuickBird data were also acquired over the New Orleans area. Following registration of the data sets, multiple high-spatial-resolution CRISP-generated hyperspectral data sets were created.

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  • Numerical Mathematics
  • Atomic and Molecular Physics and Spectroscopy

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