A Spectral Climatology for Atmospheric Compensation of Hyperspectral Imagery

July 2016
Topics: Environment, Modeling and Simulation, Sensing and Signal Processing
John H. Powell, George Mason University
Ronald G. Resmini, The MITRE Corporation
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Most Earth observation hyperspectral imagery (HSI) detection and identification algorithms depend critically upon a robust atmospheric compensation capability to correct for the effects of the atmosphere on the radiance signal. Atmospheric compensation methods typically perform optimally when ancillary ground truth data are available, e.g., high fidelity in situ radiometric observations or atmospheric profile measurements. When ground truth is incomplete or not available, additional assumptions must be made to perform the compensation. Meteorological climatologies are available to provide climatological norms for input into the radiative transfer models; however no such climatologies exist for empirical methods.

The success of atmospheric compensation methods such as the empirical line method suggests that remotely sensed HSI scenes contain comprehensive sets of atmospheric state information within the spectral data itself. It is argued that large collections of empirically-derived atmospheric coefficients collected over a range of climatic and atmospheric conditions comprise a resource that can be applied to prospective atmospheric compensation problems. A previous study introduced a new climatological approach to atmospheric compensation in which empirically derived spectral information, rather than sensible atmospheric state variables, is the fundamental datum. The current work expands the approach across an experimental archive of 127 airborne HSI datasets spanning nine physical sites to represent varying climatological conditions. The representative atmospheric compensation coefficients are assembled in a scientific database of spectral observations and modeled data.

Improvements to the modeling methods used to standardize the coefficients across varying collection and illumination geometries and the resulting comparisons of adjusted coefficients are presented. The climatological database is analyzed to show that common spectral similarity metrics can be used to separate the climatological classes to a degree of detail commensurate with the modest size and range of the imaging conditions comprising the study.

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