Hyperspectral data unmixing

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 simulate 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 allow 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

Limitations

None

Authors

OTB-Team

See also

VertexComponentAnalysis

Example of use