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Proceedings Paper

A neural network technique for atmospheric compensation and temperature/emissivity separation using LWIR/MWIR hyperspectral data
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Paper 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.

Paper Details

Date Published: 12 August 2004
PDF: 12 pages
Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, (12 August 2004); doi: 10.1117/12.543616
Show Author Affiliations
William J. Blackwell, MIT Lincoln Lab. (United States)


Published in SPIE Proceedings Vol. 5425:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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