HyperspectralUnmixing - Hyperspectral data unmixing¶

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

Detailed 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 to estimate 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¶

This section describes in details the parameters available for this application. Table [1] presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is HyperspectralUnmixing .

[1]	Table: Parameters table for Hyperspectral data unmixing.

Parameter Key	Parameter Name	Parameter Type
in	Input Image Filename	Input image
out	Output Image	Output image
ie	Input endmembers	Input image
ua	Unmixing algorithm	Choices
ua ucls	UCLS	Choice
ua ncls	NCLS	Choice
ua isra	ISRA	Choice
ua mdmdnmf	MDMDNMF	Choice
inxml	Load otb application from xml file	XML input parameters file
outxml	Save otb application to xml file	XML output parameters file

Input Image Filename: The hyperspectral data cube input.
Output Image: 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: 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: The algorithm to use for unmixing. Available choices are:

UCLS: Unconstrained Least Square.

NCLS: Non-negative constrained Least Square.

ISRA: Image Space Reconstruction Algorithm.

MDMDNMF: Minimum Dispersion Constrained Non Negative Matrix Factorization.

Load otb application from xml file: Load otb application from xml file.
Save otb application to xml file: Save otb application to xml file.

Example¶

To run this example in command-line, use the following:

otbcli_HyperspectralUnmixing -in cupriteSubHsi.tif -ie cupriteEndmembers.tif -out HyperspectralUnmixing.tif double -ua ucls

To run this example from Python, use the following code snippet:

#!/usr/bin/python

# Import the otb applications package
import otbApplication

# The following line creates an instance of the HyperspectralUnmixing application
HyperspectralUnmixing = otbApplication.Registry.CreateApplication("HyperspectralUnmixing")

# The following lines set all the application parameters:
HyperspectralUnmixing.SetParameterString("in", "cupriteSubHsi.tif")

HyperspectralUnmixing.SetParameterString("ie", "cupriteEndmembers.tif")

HyperspectralUnmixing.SetParameterString("out", "HyperspectralUnmixing.tif")
HyperspectralUnmixing.SetParameterOutputImagePixelType("out", 7)

HyperspectralUnmixing.SetParameterString("ua","ucls")

# The following line execute the application
HyperspectralUnmixing.ExecuteAndWriteOutput()

Limitations¶

None

Authors¶

This application has been written by OTB-Team.