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19 September 2016 Statistical modeling of natural backgrounds in hyperspectral LWIR data
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Hyperspectral sensors operating in the long wave infrared (LWIR) have a wealth of applications including remote material identification and rare target detection. While statistical models for modeling surface reflectance in visible and near-infrared regimes have been well studied, models for the temperature and emissivity in the LWIR have not been rigorously investigated. In this paper, we investigate modeling hyperspectral LWIR data using a statistical mixture model for the emissivity and surface temperature. Statistical models for the surface parameters can be used to simulate surface radiances and at-sensor radiance which drives the variability of measured radiance and ultimately the performance of signal processing algorithms. Thus, having models that adequately capture data variation is extremely important for studying performance trades. The purpose of this paper is twofold. First, we study the validity of this model using real hyperspectral data, and compare the relative variability of hyperspectral data in the LWIR and visible and near-infrared (VNIR) regimes. Second, we illustrate how materials that are easily distinguished in the VNIR, may be difficult to separate when imaged in the LWIR.
Conference Presentation
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Eric Truslow, Dimitris Manolakis, Thomas Cooley, and Joseph Meola "Statistical modeling of natural backgrounds in hyperspectral LWIR data", Proc. SPIE 9976, Imaging Spectrometry XXI, 99760H (19 September 2016);

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