Analysis of data from a thermal infrared hyperspectral instrument

While reflection band hyperspectral instruments have been in use for over a decade, only recently has data from airborne thermal IR hyperspectral instruments become available. One such instrument is the Airborne Hyperspectral Imager (AHI). AHI is a pushbroom sensor developed by the University of Hawaii that spans the 8 to 11.5 micrometer spectral band with 32 spectral bands and 256 simultaneous spatial channels. While many analysis techniques used for reflection band hyperspectral processing can be applied to the thermal band, new procedures had to be developed. In particular, sensor noise and sensor non-linearity induced spectral artifacts are a greater problem than for the VNIR and SWIR. The process begins with calibration, with different calibration files being used to optimize the reduction of sensor artifacts such as shading and striping. Once the data has been calibrated to radiance units, the absorption and path radiance effects of the atmosphere can be removed, if atmospheric truth is available. Following this step, the apparent emissivity is calculated for every pixel in each band. The data is now in a form that is analogous to the apparent reflectance images developed for reflection band data. At this point spectral analysis techniques can be applied to classify the image. The procedure used here was to use an automated endmember determination algorithm, N- FINDR, to determine spectral endmembers and unmix the data cube into fractional abundances. Since some endmembers are likely to result from residual sensor and cultural artifacts, the automated endmember determination and unmixing procedure is performed interactively to optimize results. Both the fractional abundance planes and the endmember spectra themselves are then reviewed for artifacts. Selected abundance planes that correspond to real minerals can then be combined into a classification map. In this paper, AHI data collected for two applications: the detection of buried land mine application and a geological remote sensing application will be presented using similar processing steps.