Atmospheric radiance interpolation for the modeling of hyperspectral data

The calibration of data from hyperspectral sensors to spectral radiance enables the use of physical models to predict measured spectra. Since environmental conditions are often unknown, material detection algorithms have emerged that utilize predicted spectra over ranges of environmental conditions. The predicted spectra are typically generated by a radiative transfer (RT) code such as MODTRAN™. Such techniques require the specification of a set of environmental conditions. This is particularly challenging in the LWIR for which temperature and atmospheric constituent profiles are required as inputs for the RT codes. We have developed an automated method for generating environmental conditions to obtain a desired sampling of spectra in the sensor radiance domain. Our method provides a way of eliminating the usual problems encountered, because sensor radiance spectra depend nonlinearly on the environmental parameters, when model conditions are specified by a uniform sampling of environmental parameters. It uses an initial set of radiance vectors concatenated over a set of conditions to define the mapping from environmental conditions to sensor spectral radiance. This approach enables a given number of model conditions to span the space of desired radiance spectra and improves both the accuracy and efficiency of detection algorithms that rely upon use of predicted spectra.