The Minimum Volume Constrained Non-negative Matrix Factorization (MVC-NMF) is an unsupervised endmember extraction algorithm. It is designed for highly mixed image data.
- create conda env with:
conda env create -f env.yml - activate env:
conda activate mvcnmf - run "python nmf_test.py"
Lidan Miao, Hairong Qi (hqi@utk.edu), EECS, University of Tennessee, Knoxville Code translated to Python by: Konstantinos Georgiou (kgeorgio@vols.utk.edu), Bredesen Center, University of Tennessee, Knoxville
L. Miao, H. Qi, "Endmember extraction from highly mixed data using minimum volume constrained non-negative matrix factorization," IEEE Transactions on Geoscience and Remote Sensing, 45(3):765-777, March 2007.
The paper received the Highest Impact Paper Award in 2012 from the IEEE Geoscience and Remote Sensing Society.