Atmospheric correction of hyperspectral imagery by statistical spectral smoothing

Ronald A. Riley; Narbik Manukian

doi:10.1117/12.454161

28 January 2002 Atmospheric correction of hyperspectral imagery by statistical spectral smoothing

Ronald A. Riley, Narbik Manukian

Proceedings Volume 4541, Image and Signal Processing for Remote Sensing VII; (2002) https://doi.org/10.1117/12.454161
Event: International Symposium on Remote Sensing, 2001, Toulouse, France

Abstract

We present a method for calculating the relative density of atmospheric gases at each spatial location of a hyperspectral image (HSI) based on a simple model of gas attenuation. These gas-density maps are used to search for gas plumes and to improve atmospheric correction of the HSI. Our premise is that the attenuation spectra of gases typically vary much more rapidly with color than the reflectance of surface materials. For a set of gas attenuation spectra, we seek the corresponding densities that provide the smoothest restored spectrum at each pixel. The attenuation spectra of common gasses can be estimated based on tabulated values for color-calibrated sensors. Our method infers the attenuation spectra of gases directly from the HSI, requiring minimal accuracy in color calibration. These inferred spectra can be compared to tabulated values to refine the color calibration of the sensor. We seek attenuation spectra that are consistent with physical models and improve the smoothness of the average spectrum. This method can also be used to search for novel gases to be recognized based on the inferred attenuation spectrum. Results are presented for AVIRIS and simulated HIRIS imagery. Removing this strongly nonlinear effect leads to significant improvements in subsequent processing.

Citation Download Citation

Ronald A. Riley and Narbik Manukian "Atmospheric correction of hyperspectral imagery by statistical spectral smoothing", Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); https://doi.org/10.1117/12.454161

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