Atmospheric effects on the classification of surface minerals in an arid region using Short-Wave Infrared (SWIR) hyperspectral imagery and a spectral unmixing technique

This study focuses on the comparison of spectral unmixing results from at-sensor radiance data and atmospherically corrected data, i.e., surface reflectance. The airborne Short-Wave Infrared (SWIR) Full Spectrum Imager (SFSI) and Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) were used to acquire data over Cuprite, Nevada, in June 1995 and 1996, respectively. This is an arid region that is well known for its exposed bedrock and alteration zones. For this project, a portion of the SWIR-2 spectral range covering the atmospheric window between 2050 and 2350 nm and a linear spectral unmixing technique were used to map surface minerals, including products of hydrothermal alteration. In total, eight end-members were extracted from the imagery using a new automatic procedure called Iterative Error Analysis (IEA). The atmospheric corrections were applied using a look-up-table procedure implemented in the Imaging Spectrometer Data Analysis System (ISDAS) and created with the radiative transfer model MODTRAN3. The fraction, or abundance, maps derived from the two types of data were compared using the coefficient of determination (R2) and the Average Euclidean Distance Coefficient (AEDC). Very good unmixing agreement was found between the results from the at-sensor radiance data and those from the surface reflectance data. For the SFSI data, the R2 values range from 0.72 to 0.95 and the AEDC values range from 0.098 to 0.023, whereas for the AVIRIS data, the R2 values range from 0.92 to 0.99 and the AEDC values range from 0.021 to 0.008. This suggests that, for the spectral range considered, atmospheric corrections are not necessary for mineral mapping in an arid region and for desert atmospheric conditions.