29 May 2017 Performance limitations of temperature–emissivity separation techniques in long-wave infrared hyperspectral imaging applications
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Abstract
Accurate estimation or retrieval of surface emissivity from long-wave infrared or thermal infrared (TIR) hyperspectral imaging data acquired by airborne or spaceborne sensors is necessary for many scientific and defense applications. This process consists of two interwoven steps: atmospheric compensation and temperature–emissivity separation (TES). The most widely used TES algorithms for hyperspectral imaging data assume that the emissivity spectra for solids are smooth compared to the atmospheric transmission function. We develop a model to explain and evaluate the performance of TES algorithms using a smoothing approach. Based on this model, we identify three sources of error: the smoothing error of the emissivity spectrum, the emissivity error from using the incorrect temperature, and the errors caused by sensor noise. For each TES smoothing technique, we analyze the bias and variability of the temperature errors, which translate to emissivity errors. The performance model explains how the errors interact to generate temperature errors. Since we assume exact knowledge of the atmosphere, the presented results provide an upper bound on the performance of TES algorithms based on the smoothness assumption.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Michael L. Pieper, Michael L. Pieper, Dimitris Manolakis, Dimitris Manolakis, Eric Truslow, Eric Truslow, Thomas W. Cooley, Thomas W. Cooley, Michael Brueggeman, Michael Brueggeman, John Jacobson, John Jacobson, Andrew Weisner, Andrew Weisner, } "Performance limitations of temperature–emissivity separation techniques in long-wave infrared hyperspectral imaging applications," Optical Engineering 56(8), 081804 (29 May 2017). https://doi.org/10.1117/1.OE.56.8.081804 . Submission: Received: 1 December 2016; Accepted: 27 April 2017
Received: 1 December 2016; Accepted: 27 April 2017; Published: 29 May 2017
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