PCOMMEND (Piecewise Convex Multiple-Model Endmember Detection and Spectral Unmixing) ReadMe
When using this code, cite it: Alina Zare, Paul Gader, & Anand Seethepalli. (2019, April 12). GatorSense/PCOMMEND: Initial Release (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.2638278
NOTE: If the PCOMMEND Algorithm is used in any publication or presentation, the following reference must be cited: Zare, A.; Gader, P.; Bchir, O.; Frigui, H., "Piecewise Convex Multiple-Model Endmember Detection and Spectral Unmixing," IEEE Transactions on Geoscience and Remote Sensing, vol.51, no.5, pp.2853-2862, May 2013.
The command to run the PCOMMEND code:
[E,P,U]=PCOMMEND(inputData,parameters)
The inputData is a NxD matrix of N input data points each of D spectral bands (dimensionality).
The parameters input is a struct with the following fields:
parameters.alpha % Regularization Parameter. Range of alpha should be between 0 and 1.
parameters.changeThresh % Stopping criteria for the objective function.
parameters.M % Number of Endmembers per endmember set.
parameters.iterationCap % Maximum number of iterations.
parameters.C % Number of clusters or number of endmember sets.
parameters.m % Fuzzifier for membership.
parameters.EPS=.0001; % Small positive constant.
The parameters structure can be generated using the PCOMMEND_Parameters.m function.
The parameters must be adjusted for a particular data set. Generally, m (the fuzzifer) is set to between 1.1 and 2 (usually, 2) depending on the data set. Alpha (the regularization parameter) is generally set to a value between 0.001 and 0.1 depending on the data set. We have also found that PCOMMEND has improved performance if the data has been normalized between 0 and 1 before running PCOMMEND (e.g. subtracting the minimum and then dividing by the max OR normalizing each spectrum by its L2 norm, depending on the application and data set).**
Simulated Hyperspectral Data Demo:
To Run the PCOMMEND Algorithm, with the example simulated hyperspectral data set run the scipt -
"PCOMMEND_RunSimulatedData.m"
This script will estimate endmembers and proportions for the simulated hyperspectral data set and plot the results.
Run the PCOMMEND Algorithm on a Hyperspectral Image Cube:
Load an HSI image cube into a variable named: Image and then run: PCOMMEND_RunRealData
NOTE: You will need to vary the input parameters for real hyperspectral data set. As the default values listed in the current code is for our specific simulated hyperspectral data set.
If you have any questions, please contact:
Alina Zare
Electrical and Computer Engineering
University of Florida
azare [at] ufl.edu