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Proceedings Paper

Predictive subpixel spatial/spectral modeling using fused HSI and MSI data
Author(s): Fred A. Kruse
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Paper Abstract

Hyperspectral Imagery (HSI) data are inherently more expensive and difficult to acquire than Multispectral Imagery (MSI) data. Most HSI acquisitions are currently limited to relatively low spatial resolution with the inherent limitation that they are not able to spatially resolve many important targets. Higher spatial resolution MSI data are available, however, they typically don’t provide sufficient spectral resolution to allow unambiguous identification of specific targets. We describe a new method using predictive modeling of combined HSI/MSI data to generate simulated high-spatial- resolution, high-spectral-resolution image data. Targets observed by the low spatial resolution HSI sensor are spectrally resolved through linear spectral unmixing, but not spatially resolved. Targets observed by higher spatial resolution MSI sensor are partially spatially resolved but lower spectral resolution doesn’t allow target identification. The fused data are used to predict the subpixel locations of HSI-identified subpixel targets within the superior MSI spatial resolution. Comparison of individual HSI endmember spectra to MSI signatures at multiple MSI pixels allows mapping the spatial locations of specific HSI endmembers creating a simulated HSI dataset that allows location of spectrally resolved targets. The combined data incorporate the best features of HSI/MSI to allow improved target detection and characterization.

Paper Details

Date Published: 12 August 2004
PDF: 11 pages
Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, (12 August 2004); doi: 10.1117/12.542631
Show Author Affiliations
Fred A. Kruse, Horizon GeoImaging, LLC (United States)


Published in SPIE Proceedings Vol. 5425:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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