Adaptive multidimensional Wiener filtering for target detector improvement

In this paper, we consider the problem of hyperspectral image denoising. Current denoising is based on multichannel restoration filters assuming the separability of the signal covariance, which describes the between-channel and within-channel relationships. We propose a new algorithm for a spectral band restoration scheme, the adaptive multidimensional Wiener filter, based on a local signal model, without assuming spectral and spatial separability. The proposed filter can be applied as a preprocessing step for detection in hyperspectral imagery. We highlight the target detection improvement when the developed method is used before existing methods the well-known hyperspectral imagery detectors as: AMF (Adaptive Matched Filter), ACE (Adaptive coherence/cosine Estimator) and RX (Reed and Xiaoli algotithm). We demonstrate that integrating a multidimensional restoration leads to significant improvement of the detection probability. The performance of our method is exemplified using real-world HYDICE images.