Reducing the effects of space-varying, wavelength-dependent scattering in multispectral imagery

A new method for reducing the effects of space-varying, wavelength-dependent scattering in multispectral imagery caused by smoke and haze is described. It is intended for use in situations where atmospheric scattering affects the shorter wavelengths and varies in space. The method converts an image in which space-varying scattering is present into an image where the scattering has been equalized over the entire image, so that previously developed techniques for removing constant scattering effects can be used. The spectral measurement space is viewed as consisting of two subspaces: one spanned by the bands that are affected by scattering, the other by the bands that are not. A correspondence between the two subspaces is established and used to predict the values of the former bands (i.e., what their values would be without scattering) from the latter. Our haze equalization algorithm is compared to an earlier de-hazing algorithm developed by Lavreau that subtracts a portion of the fourth tasselled cap feature, used as an estimate of the atmospheric component, from the visible bands. While both are shown to be effective in removing space-varying smoke and haze, the de-hazing algorithm tends to remove subtle detail and increases the spectral correlation between the visible bands, while the haze-equalization algorithm preserves subtle detail and maintains the spectral balance between bands.