Temperature estimation and compositional mapping using spectral mixture analysis of thermal imaging spectrometry data

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Abstract
In the thermal infrared (TIR), a surface emits radiation based on its temperature and emissivity. TIR imaging spectrometry involves extracting temperature, emissivity, and/or surface composition information, which are useful in a wide variety of studies ranging from climatology to land use analyses. A simple technique of temperature emissivity separation (TES) has been developed to separate the effects of emissivity from temperature within the radiance signal recorded by a sensor. This technique can be employed to map the composition and temperature of a surface. Likewise, spectral mixture analysis (SMA) has been successfully applied in this spectral region to discern spectral features and their temperatures. This paper describes an application of TES and SMA that has been employed to characterize the isothermal combinations of thermal radiance features on a sub-pixel basis. This approach, referred to in this paper as a Temperature Emissivity Separation Spectral Mixture Analysis (TESSMA), uses the relationship between a 'virtual cold' endmember fraction and surface temperature to extract image temperature estimates, which are then used to constrain an isothermal unmixing of pixel endmembers. Work presented includes characterizations of synthetically generated temperature-endmember fraction test images, a discussion of methods used to separate temperature and endmember attributions, and a fraction estimate analysis. This paper also demonstrates the temperature dependence of isothermal SMA on accurate temperature estimates, two TESSMA approaches, and their results.

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