Residual striping reduction in hyperspectral images

In this paper a new algorithm for striping noise reduction in hyperspectral images is proposed. Signal dependent striping noise is reduced by exploiting the high degree of spectral correlation of the useful signal in hyperspectral data. The algorithm does not require the human intervention nor introduces significant radiometric distortions on the useful signal. Results obtained on simulated and real hyperspectral images are presented and discussed. The performance of the method is evaluated through established used indexes quantifying both the striping reduction and the radiometric distortion introduced on the image.