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

Input Image Filename (in): The hyperspectral data cube input
Output Image (out): The output abundance map. The abundance fraction are stored in a multispectral image where band N corresponds to the fraction of endmembers N in each pixel.
Input endmembers (ie): The endmembers (estimated pure pixels) to use for unmixing. Must be stored as a multispectral image, where each pixel is interpreted as an endmember.
Unmixing algorithm (ua): The algorithm to use for unmixing
- UCLS (ucls): Unconstrained Least Square
- NCLS (ncls): Non-negative constrained Least Square
- ISRA (isra): Image Space Reconstruction Algorithm
- MDMDNMF (mdmdnmf): Minimum Dispersion Constrained Non Negative Matrix Factorization

Load otb application from xml file (inxml): Load otb application from xml file
Save otb application to xml file (outxml): Save otb application to xml file

Limitations

None

Authors

OTB-Team