Hyperspectral LWIR automated separation of surface emissivity and temperature (ASSET)

Joan N. Hayashi; Mary H. Sharp

doi:10.1117/12.451544

8 November 2002 Hyperspectral LWIR automated separation of surface emissivity and temperature (ASSET)

Joan N. Hayashi, Mary H. Sharp

Proceedings Volume 4816, Imaging Spectrometry VIII; (2002) https://doi.org/10.1117/12.451544
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

Abstract

ASSET is based on physical first principles and was developed using synthetic data. The method treats each pixel independently, assumes homogeneous, isothermal pixels and requires the following inputs: 1) Hyperspectral LWIR radiance imagery, 2) Atmospheric parameters (downwelling irradiance, upwelling radiance, and transmissivity), and 3) A library of material emissivities. For each pixel, the method determines the most appropriate material from the emissivity library. The method computes the pixel temperature assuming pure pixels. Then, the pixel temperature is used to determine the emissivity. Note that the computed emissivity may differ from that of the selected library material due to a variety of factors such as noise, mixed pixels, natural spectral variability, and inadequate atmospheric compensation. The synthetic data used to develop ASSET were constructed by computing the thermally emitted radiances of a set of materials with specificed emissivities at a range of temperatures. A given set of atmospheric parameters was then applied to the radiances to obtain at-aperature radiance. Random additive gaussian noise was applied to the data. ASSET was run using the synthetic data, as well as additional materials. The initial results from ASSET are promising. With a signal-to-noise ratio (SNR) of 500, the material was correctly classified 100% of the time. The mean absolute temperature error for this case was 0.02 K with a standard deviation of 0.02. The maximum absolute temperature error was 0.12 K. With a SNR of 300, the material was correctly classified more than 99% of the time. The mean absolute temperature error for this case was 0.04 K with a standard deviation of 0.03. The maximum absolute temperature error was 1.07 K. We present results from a simple synthetic data set as well as results from applying ASSET to more sophisticated synthetic DIRSIG LWIR imagery.

Citation Download Citation

Joan N. Hayashi and Mary H. Sharp "Hyperspectral LWIR automated separation of surface emissivity and temperature (ASSET)", Proc. SPIE 4816, Imaging Spectrometry VIII, (8 November 2002); https://doi.org/10.1117/12.451544

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