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 allows to estimate 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] Input Image Filename (-in): The hyperspectral data cube to unmix
[param] Output Image (-out): The output abundance map
[param] 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
[choice] Unmixing algorithm (-ua): The algorithm to use for unmixing
- [group] UCLS: Unconstrained Least Square
- [group] FCLS: Fully constrained Least Square
- [group] NCLS: Non-negative constrained Least Square
- [group] ISRA: Image Space Reconstruction Algorithm
- [group] MDMDNMF: Minimum Dispersion Constrained Non Negative Matrix Factorization

Limitations

None

Authors

OTB-Team