A Spectral Climatology for Atmospheric Compensation of Hyperspectral Imagery
Date
2015
Authors
Powell, John H.
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Abstract
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. This research introduces a new climatological approach to atmospheric compensation in which empirically derived spectral information, rather than sensible atmospheric state variables, is the fundamental datum. An experimental archive of airborne HSI data is mined for representative atmospheric compensation coefficients, which are assembled in a scientific database of spectral observations and modeled data. The empirical techniques for extracting the coefficients and correcting for small nonlinear features, the modeling methods used to standardize the coefficients across varying collection and illumination geometries, and the resulting comparisons of adjusted coefficients are presented. The resulting 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. The study closes with a notional application example and a discussion of the potential benefits, shortfalls and future work to fully develop the new technique.
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Keywords
Remote sensing, Atmospheric sciences, Atmospheric compensation, Empirical line method, HSI, Hyperspectral, Remote sensing