Minimizing the impact of signal-dependent noise on hyperspectral target detection

Abstract

Multilinear algebra based method for noise reduction in hyperspectral images (HSI) is proposed to minimize negative impacts on target detection of signal-dependent noise. A parametric model, suitable for HSIs that the photon noise is dominant compared to the electronic noise contribution, is used to describe the noise. To diminish the data noise from hyperspectral images distorted by both signal-dependent (SD) and signal-independent (SI) noise, a tensorial method, which reduces noise by exploiting the different statistical properties of those two types of noise, is proposed in this paper. This method uses a parallel factor analysis (PARAFAC) decomposition to remove jointly SI and SD noises. The performances of the proposed method are assessed on simulated HSIs. The results on the real-world airborne hyperspectral image HYDICE (Hyperspectral Digital Imagery Collection Experiment) are also presented and analyzed. These experiments have demonstrated the benefits arising from using the pre-whitening procedure in mitigating the impact of the SD in different detection algorithms for hyperspectral images.