Accurate estimation of surface emissivity from long-wave infrared (LWIR) hyperspectral imaging data acquired by airborne or space-borne sensors is necessary for many scientific and defense applications. This process consists of two interwoven steps: atmospheric compensation (AC) and temperature-emissivity separation (TES). AC uses an estimated atmospheric model to estimate the ground radiance from the at-aperture radiance, and then TES produces a temperature and emissivity estimate using this ground radiance. Hyperspectral TES algorithms assume that the emissivity spectra for solids are smooth compared to the atmosphere. The quality of TES estimates depend on the accuracy of the atmospheric estimates and their band-averaging to the sensor spectral resolution. The objective of this paper is to study the sensitivity of TES results to errors in atmospheric estimates. There are several errors associated with TES output including temperature errors, and shape, bias, and smoothness errors in the emissivity. The effects of atmospheric errors are studied by using MODTRAN to generate models with varying temperature and water vapor amounts. One model is used to simulate at-aperture radiances while the others are used for AC and TES on the simulated spectra. The mismatch between the models affects the TES results. While most errors cause TES to fail providing rough emissivity estimates, magnitude and shape errors provide smooth but incorrect emissivities. Understanding how atmospheric errors affect the TES estimates and what is required of atmospheric estimates to provide smooth emissivity estimates will help in developing algorithms to find the correct atmosphere for a scene.
M. L. Pieper, D. Manolakis, E. Truslow, J. Jacobson, A. Weisner, T. Cooley, and V. Ingle, "Sensitivity of temperature and emissivity separation to atmospheric errors in LWIR hyperspectral imagery," Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 106440R (Presented at SPIE Defense + Security: April 18, 2018; Published: 8 May 2018); https://doi.org/10.1117/12.2303877.
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