12 August 2004 A neural network technique for atmospheric compensation and temperature/emissivity separation using LWIR/MWIR hyperspectral data
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Abstract
A novel statistical method for the retrieval of surface temperature and atmospheric temperature, moisture, and ozone profiles has been developed and evaluated with simulated clear-air hyperspectral data at 3.5-5 and 7-13.5 microns. These estimates are used as inputs to MODTRAN to calculate the ground leaving radiance (L), the upwelling radiance (U), and the total path transmittance (T). The spectral surface emissivity is then derived by spectrally filtering the resulting solution to the radiative transfer equation. The retrieval for surface temperature and spectral surface emissivity can then be iterated, if necessary. The method was evaluated using the NOAA88b profile dataset and the UCSB and ASTER spectral emissivity libraries, and the sensor parameters were developed using the FASSP (Forecasting and Analysis of Spectroradiometer System Performance) model. Representative results are shown for simulated data from two spaceborne sensors: a high spectral resolution infrared sensor (AIRS, NASA Aqua satellite, 3.5-15.5 μm, λ/Δλ ≈ 1200) and a hypothetical moderate spectral resolution infrared sensor (3.5-5 and 7-13.5 μm, λ/Δλ ≈ 200). A sample retrieval of surface temperature and emissivity is also shown.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
William J. Blackwell, William J. Blackwell, } "A neural network technique for atmospheric compensation and temperature/emissivity separation using LWIR/MWIR hyperspectral data", Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, (12 August 2004); doi: 10.1117/12.543616; https://doi.org/10.1117/12.543616
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