# HyperspectralUnmixing

## Brief Description

Estimate abundance maps from an hyperspectral image and a set of endmembers.

## Tags

Hyperspectral

## Long Description

The application applies a linear unmixing algorithm to an hyperspectral data cube. This method supposes that the mixture between materials in the scene is macroscopic and simulates a linear mixing model of spectra. The Linear Mixing Model (LMM) acknowledges that reflectance spectrum associated with each pixel is a linear combination of pure materials in the recovery area, commonly known as endmembers. Endmembers can be estimated using the VertexComponentAnalysis application. The application allows estimating the abundance maps with several algorithms : Unconstrained Least Square (ucls), Fully Constrained Least Square (fcls), Image Space Reconstruction Algorithm (isra) and Non-negative constrained Least Square (ncls) and Minimum Dispersion Constrained Non Negative Matrix Factorization (MDMDNMF).

## Parameters

- [param] -in <string> The hyperspectral data cube to unmix. Mandatory: True. Default Value: ""
- [param] -out <string> The output abundance map. Mandatory: True. Default Value: ""
- [param] -ie <string> The endmembers (estimated pure pixels) to use for unmixing. Must be stored as a multispectral image, where each pixel is interpreted as an endmember. Mandatory: True. Default Value: ""
[choice] -ua The algorithm to use for unmixing ucls,ncls,isra,mdmdnmf. Mandatory: False. Default Value: "ucls"

- [group] -ucls

- [group] -ncls

- [group] -isra

- [group] -mdmdnmf

## Limitations

None

## Authors

OTB-Team

## See Also

VertexComponentAnalysis

## Example of use

- in: cupriteSubHsi.tif

- ie: cupriteEndmembers.tif

- out: HyperspectralUnmixing.tif double

- ua: ucls