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Estimate abundance maps

estimates the abundance maps of the endmembers in a hyperspectral data cube by using the
least-squares method.`abundanceMap`

= estimateAbundanceLS(`inputData`

,`endmembers`

)

A hyperspectral data cube can contain both pure and mixed pixels. Pure pixels exhibit
the spectral characteristics of a single class, while the mixed pixels exhibit the spectral
characteristics of multiple classes. The spectral signatures of the pure pixels comprise the
endmembers that identify the unique classes present in a hyperspectral data cube. The
spectral signature of mixed pixels can be a linear combination of two or more endmember
spectra. The abundance map identifies the proportion of each endmember present in the
spectra of each pixel. For a hyperspectral data cube of spatial dimensions
*M*-by-*N* containing *P* endmembers,
there exist *P* abundance maps, each of size
*M*-by-*N*.

The abundance map estimation process is known as *spectral unmixing*,
which is the decomposition of the spectra of each pixel into a given set of endmember
spectra.

specifies the least-squares method to use for estimating the abundance maps.`abundanceMap`

= estimateAbundanceLS(___,'Method',`estMethod`

)

**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] Keshava, N., and J.F. Mustard.
“Spectral Unmixing.” *IEEE Signal Processing Magazine* 19,
no. 1 (January 2002): 44–57. https://doi.org/10.1109/79.974727.

[2] Kay, Steven M. *Fundamentals of Statistical Signal Processing*. Prentice Hall
Signal Processing Series. Englewood Cliffs, N.J: Prentice-Hall PTR, 1993.