Hyperspectral data unmixing

Brief Description

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

Tags

Miscellaneous, Hyperspectral

Long Description

The application applies a linear unmixing algorithmto an hyperspectral data cube. This method supposes that the mixture betweenaterials in the scene is macroscopic and simulates a linear mixing model ofspectra.

The Linear Mixing Model (LMM) acknowledges that reflectancespectrum associated with each pixel is a linear combination of purematerials in the recovery area, commonly known as endmembers. Endmembers canbe estimated using the VertexComponentAnalysis application.

The application allows estimating the abundance maps with several algorithms :
* Unconstrained Least Square (ucls)
* Image Space Reconstruction Algorithm (isra)
* Non-negative constrained
* Least Square (ncls)
* Minimum Dispersion Constrained Non Negative Matrix Factorization (MDMDNMF).

Parameters

Limitations

None

Authors

OTB-Team

See also

VertexComponentAnalysis

Example of use