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Maximum noise fraction transform of hyperspectral data

computes specified number of principal component bands `outputDataCube`

= hypermnf(`inputData`

,`numComponents`

)`numComponents`

by
using the maximum noise fraction (MNF) transform. To achieve spectral dimensionality
reduction, the specified number of principal components must be less than the number of
spectral bands in the input data cube.

The components derived using MNF transform are also called non-adjusted principal components and the MNF transform arranges principal components (PC) in the decreasing order of PC image quality.

`[`

also returns the MNF coefficients estimated across the spectral bands of the input data
cube.`outputDataCube`

,`coeff`

] = hypermnf(`inputData`

,`numComponents`

)

`[___] = hypermnf(`

computes MNF transform from mean centered spectral bands. The option for mean centering each
spectral band in the input data cube is specified by `inputData`

,`numComponents`

,'MeanCentered',`flag`

)`flag`

.

**Note**

This function requires the Image Processing Toolbox™ Hyperspectral Imaging Library. You can install the Image Processing Toolbox Hyperspectral Imaging Library from Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons.

[1] Green, A.A., M. Berman, P.
Switzer, and M.D. Craig. “A Transformation for Ordering Multispectral Data in Terms of Image
Quality with Implications for Noise Removal.” *IEEE Transactions on
Geoscience and Remote Sensing* 26, no. 1 (January 1988): 65–74.
https://doi.org/10.1109/36.3001.