Adaptive-3D-Wiener for hyperspectral image restoration: Influence on detection strategy

In this paper we consider the problem of multichannel restoration. Current multichannel least squares restoration filters utilize the assumption that the signal autocorrelation, describing the between-channel and within-channel relationship, is separable. We propose a Wiener solution for a multichannel restoration scheme, the Adaptive-3D-Wiener filter, based on a local signal model, without using the assumption of spectral and spatial separability. Moreover, when the number of channels is superior to 3, the restoration is in many cases the preprocessing to a given application such as classification, segmentation or detection, so it seems to be important to perform a restoration which suits to the application in fine. In this aim, the proposed filter is developed to be used as a preprocessing step for detection in hyperspectral imagery. Tests on real data show that the proposed filter enables to enhance detection performance in target detection and anomaly detection applications with two well-known detection algorithms in hyperspectral imagery.