Estimate abundance maps from an hyperspectral image and a set of endmembers.
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 one 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).
This section describes in details the parameters available for this application. Table 4.156, page 789 presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is HyperspectralUnmixing.
Parameter key | Parameter type |
Parameter description |
in | Input image |
Input Image Filename |
out | Output image |
Output Image |
ie | Input image |
Input endmembers |
ua | Choices |
Unmixing algorithm |
ua ucls | Choice |
UCLS |
ua ncls | Choice |
NCLS |
ua isra | Choice |
ISRA |
ua mdmdnmf | Choice |
MDMDNMF |
inxml | XML input parameters file |
Load otb application from xml file |
outxml | XML output parameters file |
Save otb application to xml file |
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To run this example in command-line, use the following:
To run this example from Python, use the following code snippet:
None
This application has been written by OTB-Team.
These additional ressources can be useful for further information: