[IP1-7-2-1] Minimum noise fraction (MNF)

A method for dimensionality reduction in hyperspectral data is Minimum Noise Fraction (MNF). The purpose is to minimize the noise in the imagery, i.e. to identify noise and segregate it from true information, and to colaps the useful information into a much smaller set of MNF images. The MNF transformation applies two cascaded principal components analyses.

External resources

• Green, A.A., M. Berman, P. Switzer, and M.D. Craig, 1988, A Transform for Ordering Multispectral Data in terms of Image Quality with Implications for Noise Removal, IEEE Trans. Geosci. Remote Sens., vol. 26, No. 1, pp. 65-74.
• Jensen, J. R. (2005). Introductory digital image processing : a remote sensing perspective (3rd ed.). Upper Saddle River, N.J.: Prentice Hall, p. 443-444.

Learning outcomes

• 1145 - Explain how minimum noise fraction makes use of principal components analysis for dimensionality reduction

  Explain how minimum noise fraction makes use of principal components analysis for dimensionality reduction

Self assessment

Outgoing relations

• [IP1-7-2-1] Minimum noise fraction (MNF) subclassof [IP1-7-2] Dimensionality reduction

Incoming relations

• [IP1-6] Principal component analysis (PCA) is prerequisite of [IP1-7-2-1] Minimum noise fraction (MNF)