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18 September 2018 Wavelength calibration correction for ground radiance spectra in LWIR hyperspectral imagery
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Accurate retrieval of surface emissivity from long-wave infrared (LWIR) hyperspectral imaging data is necessary for many scientific and defense applications. Emissivity estimation consists of two interwoven steps: atmospheric compensation (AC) and temperature-emissivity separation (TES). AC uses an atmospheric estimate to convert the at-aperture radiance to ground radiance. Using the ground radiance, TES produces a temperature and emissivity estimate. TES algorithms require an accurate atmospheric model, and assumes that emissivity spectra for solids are smooth, compared to atmospheric features. A high-resolution atmospheric model is band-averaged to the sensor's spectral response function (SRF). Characterization and maintenance of the SRF is difficult, and errors cause rough emissivity estimates. We propose a method where spectra with smooth reflective emissivities are used to correct errors from the SRF. In-Scene AC (ISAC) methods can be used to find accurate estimates of the band-averaged atmospheric upwelling and transmission, but not the downwelling radiance which is needed for TES. Typical TES methods use a model for the downwelling radiance and an assumed SRF, which will differ from the true SRF causing unnaturally rough emissivity estimates. While ISAC estimates include the true SRF it is difficult to separate the SRF from these measurements. Instead of estimating the SRF directly, our method uses smooth low emissivity materials to produce a correction for the downwelling radiance that matches the true band-averaged values. We demonstrate this technique using simulated data.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
M. L. Pieper, D. Manolakis, E. Truslow, J. Jacobson, A. Weisner, T. Cooley, and V. Ingle "Wavelength calibration correction for ground radiance spectra in LWIR hyperspectral imagery", Proc. SPIE 10768, Imaging Spectrometry XXII: Applications, Sensors, and Processing, 1076805 (18 September 2018);

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