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Mitigation of atmospheric effects in hyperspectral data analysis

For hyperspectral data analysis, the general objective for atmospheric compensation algorithms is to remove solar illumination and atmospheric effects from the measured spectral data so that surface reflectance can be retrieved. This then allows for comparison with library data for target identification. Recent advances in spectral sensing capability have led to the development of a number of atmospheric compensation algorithms for hyperspectral data analysis. In this paper, three topics will be discussed: (1) algorithm evaluation of two physics-based approaches: ATREM and the AFRL model, (2) sensitivity analysis of the effects of various input parameters to surface reflectance retrieval, and (3) algorithm enhancements of how water vapor and aerosol retrievals can be better conducted than current algorithms. Examples using existing hyperspectral data, including those from HYDICE, AVIRIS will be discussed. Results will also be compared with truth information derived from ground and satellite based meteorological data.