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 spectral signatures, however, spatial coverage is limited. Multispectral Imaging (MSI) data are spectrally undersampled and may not allow unique identification, but they do provide synoptic spatial coverage. We have developed an approach that uses coincident HSI/MSI data to extend mineral mapping to larger areas. Hyperspectral data are used to model and extend signatures to multispectral Advanced Spaceborne Thermal Emmission and Reflection Radiometer (ASTER) data. Analysis consists of 1. Atmospheric correction of both the hyperspectral and multispectral data, 2. Analysis of the hyperspectral data to determine spectral endmembers and their spatial distributions, 3. Spectral modeling to convert the hyperspectral signatures to the multispectral response, and 4. Analysis of the MSI data to extend mapping to the larger spatial coverage of the multispectral data. Comparing overlapping area with extensive field verification shows that ASTER mineral mapping using these methods approaches 70% accuracy compared to HSI for selected minerals. Spot checking of extended ASTER mapping results also shows good correspondence. While examples shown are specific to ASTER Short Wave Infrared (SWIR) data, the approach could also be used for other multispectral sensors and spectral ranges.