Integrated visible and near-infrared, shortwave infrared, and longwave infrared full-range hyperspectral data analysis for geologic mapping

Fred A. Kruse

doi:10.1117/1.JRS.9.096005

23 September 2015 Integrated visible and near-infrared, shortwave infrared, and longwave infrared full-range hyperspectral data analysis for geologic mapping

Fred A. Kruse

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 096005 (September 2015). https://doi.org/10.1117/1.JRS.9.096005

Abstract

Airborne visible/infrared imaging spectrometer (AVIRIS) and spatially coincident hyperspectral thermal emission spectrometer (HyTES) data were used to map geology and alteration for a site in northern Death Valley, California and Nevada. AVIRIS with 224 bands from 0.4 to 2.5 μm were converted to reflectance. HyTES data with 256 bands covering 8 to 12 μm were converted to emissivity. Two approaches were investigated for integration of the datasets for full spectrum analysis. A combined (integrated) bands method utilized 332 spectral bands spanning both datasets. Spectral endmembers were extracted, and the predominant material at each pixel was mapped for the full spectral range using partial unmixing. This approach separated a variety of materials, but it was difficult to directly relate mapping results to surface properties. The second method used visible to near-infrared, shortwave infrared, and longwave infrared data independently to determine and map key endmembers in each spectral range. AVIRIS directly mapped a variety of specific minerals, while HyTES separated and mapped several igneous rock phases. Individual mapping results were then combined using geologically directed logical operators. The full-range results illustrate that integrated analysis provides advantages over use of just one spectral range, leading to improved understanding of the distribution of geologic units and alteration.

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Fred A. Kruse "Integrated visible and near-infrared, shortwave infrared, and longwave infrared full-range hyperspectral data analysis for geologic mapping," Journal of Applied Remote Sensing 9(1), 096005 (23 September 2015). https://doi.org/10.1117/1.JRS.9.096005

Published: 23 September 2015

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