The effect of noise whitening on methods for determining the intrinsic dimension of a hyperspectral image

Abstract

Determining the intrinsic dimension of a hyperspectral image is an important step in the spectral unmixing process, and under- or over- estimation of this number may lead to incorrect unmixing for unsupervised methods. It is known that most real images contain noise that is not i.i.d. across bands, and so methods that assume i.i.d. noise are often avoided. However, this problem may be alleviated by implementing a noise whitening procedure as a pre-processing step. In this paper we will investigate one particular noise whitening approach, as well as a noise removal approach, and consider how the application of these methods may improve several methods for determining the intrinsic dimension of an image, including Malinowski’s Empirical Indicator Function [1], Random Matrix Theory [2], and Harsanyi-Farrand-Chang [3].