The definition of noise models suitable for hyperspectral data is slightly different depending on whether whiskbroom or push-broom are dealt with. Focussing on the latter type (e.g., VIRS-200) the noise is intrinsically non-stationary in the raw digital counts. After calibration, i.e. removing the variability effects due to different gains and offsets of detectors, the noise will exhibit stationary statistics, at least spatially. Hence, separable 3D processes correlated across track (x), along

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... rack (y) and in the wavelength (?), modelled as auto-regressive with GG statistics have been found to be adequate. Estimation of model parameters from the true data is accomplished through robust techniques relying on linear regressions calculated on scatter-plots of local statistics. An original procedure was devised to detect areas within the scatter-plot corresponding to statistically homogeneous pixels. Results on VIRS-200 data show that the noise is heavy-tailed (tails longer than those of a Gaussian PDF) and somewhat correlated along and across track by slightly different extents. Spectral correlation has been investigated as well and found to depend both on the sparseness (spectral sampling) and on the wavelength values of the bands that have been selected.