Hyperspectral data unmixing¶

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

Detailed 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 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).

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 Type	Parameter Description
in	Input image	Input image
out	Output image	Output image
ie	Input image	Input image
ua	Choices	Choices
ua ucls	Choice	UCLS
ua ncls	Choice	NCLS
ua isra	Choice	ISRA
ua mdmdnmf	Choice	MDMDNMF
inxml	XML input parameters file	XML input parameters file
outxml	XML output parameters file	XML output parameters file

Input Image Filename: The hyperspectral data cube to unmix.
Output Image: The output abundance map.
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.