Improving multispectral mapping by spectral modeling with hyperspectral signatures

Abstract

Hyperspectral imaging (HSI) data in the 0.4 - 2.5 micrometer spectral range allow direct identification of materials using their fully resolved spectral signatures, however, spatial coverage is limited. Multispectral Imaging data (MSI) are spectrally undersampled and may not allow unique identification, but they do provide synoptic spatial coverage. This paper summarizes an approach that uses coincident HSI/MSI data to extend HSI results to cover larger areas. Airborne Visible/Infrared (AVIRIS)/Hyperion and multispectral ASTER/MASTER data supported by field spectral measurements are used to allow modeling and extension of hyperspectral signatures to multispectral data. Full-scene mapping using the modeled signatures allows subsequent mapping of extended areas using the multispectral data. Both the hyperspectral and multispectral data are atmospherically corrected using commercial-off-the-shelf (COTS) atmospheric correction software. Hyperspectral data are then analyzed to determine spectral endmembers and their spatial distributions, and validated using the field spectral measurements. Spectral modeling is used to convert the hyperspectral spectral signatures to the multispectral data response. Reflectance calibrated multispectral data are then used to extend the hyperspectral mapping to the larger spatial coverage of the multispectral data. Field verification of mapping results is conducted and accuracy assessment performed. Additional sites are assessed with multispectral data using the modeling methodology based on scene-external HSI and/or field spectra (but without scene-specific a priori hyperspectral analysis or knowledge). These results are further compared to field measurements and subsequent hyperspectral analysis and mapping to validate the spectral modeling approach.