Estimation of correlated signal-dependent noise statistics in hyperspectral images

ABSTRACT

Accurate estimation of the underlying noise statistics is vital for the good performance of many hyperspectral image processing algorithms.Our proposed method can be used to estimate the full covariance matrix of the noise in the case where spectral correlation is present in the noise. However, this did not cater for the case of signal-dependent noise. In this paper, we extend the framework already proposedto the case of signal-dependent noise and propose a method to estimate the parameters of the signal-dependent noise variance even if the noise is spectrally correlated. It is shown that the proposed method can accurately estimate the different noise parameters in artificial datasets and even in the uncorrelated case usually gives better performance than the existing methods. Finally, the method is also applied and analysed in real datasets.

Acknowledgments

The authors would like to thank Nicola Acito for sharing the Matlab code for his proposed method, TerraCore Inc. (WeblinkTerracore. n.d.) for sharing real hyperspectral datasets.

Data Availability Statement

The data that support the findings of this study are openly available at (WeblinkData n.d.).