A Spectral Climatology for Atmospheric Compensation

May 2014
Topics: Meteorological Factors, Remote Sensing, Signal Processing, Image Processing
Ronald G. Resmini, The MITRE Corporation
John H. Powell, George Mason University
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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. Most atmospheric compensation methods 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 paper 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 and sensible atmospheric observations. We present the empirical techniques for extracting the coefficients, the modeling methods used to normalize the coefficients across varying collection and illumination geometries, and the resulting comparisons of adjusted coefficients. Preliminary results comparing normalized coefficients from representative scenes across several distinct environments are presented, along with a discussion of the potential benefits, shortfalls, and future work to fully develop the new technique.​

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