Instruction on how to install all the required packages and libraries. The instructions given are for linux environments (specifically Ubuntu) as this was the environment used in development. For other OS'es the steps should be similar.
As a prerequisite Matlab needs to be installed on the machine.
For Python installation pipenv virtual environment is used. To install you need pip and pipenv packages for Python.
Only pip can be used or with virtualenv by installing packages from requirements.txt
Then run (you can specify python version with --python 3.x):
pipenv installEnter the environment with:
pipenv shellThen Matlab engine needs to be install into this python environment (official tutorial https://ch.mathworks.com/help/matlab/matlab_external/install-the-matlab-engine-for-python.html):
write permissions to folders build and dist (inside ../extern/engines/python/ folder) are required to install the pacakge successfully.
cd /path/to/MATLAB/R202xb/extern/engines/python/
python3 -m pip install .Test the matlab installation with these commands
python3
import matlab.engine
eng = matlab.engine.start_matlab()for matlab "Statistics and Machine Learning Toolbox" is required
https://codeocean.com/capsule/2018026/tree/v2
Wang, Xinyu & Zhong, Yanfei & Zhang, Liangpei & Xu, Yanyan. (2017). Spatial Group Sparsity Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-18. 10.1109/TGRS.2017.2724944.
https://github.com/danfenghong/ALMM_TIP
D. Hong, N. Yokoya, J. Chanussot and X. X. Zhu, "An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing," in IEEE Transactions on Image Processing, vol. 28, no. 4, pp. 1923-1938, April 2019, doi: 10.1109/TIP.2018.2878958.
https://naotoyokoya.com/Download.html
N. Yokoya, T. Yairi and A. Iwasaki, "Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion," in IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 2, pp. 528-537, Feb. 2012, doi: 10.1109/TGRS.2011.2161320.
http://www.lx.it.pt/~bioucas/code.htm
J. Li, J. M. Bioucas-Dias, A. Plaza and L. Liu, "Robust Collaborative Nonnegative Matrix Factorization for Hyperspectral Unmixing," in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 10, pp. 6076-6090, Oct. 2016, doi: 10.1109/TGRS.2016.2580702.
http://www.lx.it.pt/~bioucas/code.htm
J. M. Bioucas-Dias and M. A. T. Figueiredo, "Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing," 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Reykjavik, Iceland, 2010, pp. 1-4, doi: 10.1109/WHISPERS.2010.5594963.
http://www.lx.it.pt/~bioucas/code.htm
M. -D. Iordache, J. M. Bioucas-Dias and A. Plaza, "Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing," in IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 11, pp. 4484-4502, Nov. 2012, doi: 10.1109/TGRS.2012.2191590.
Algorithm is part of the repository https://github.com/ricardoborsoi/MUA_SparseUnmixing
S. Zhang, J. Li, H.-C. Li, C. Deng and A. Plaza. Spectral-Spatial Weighted Sparse Regression for Hyperspectral Image Unmixing. IEEE Transactions on Geoscience and Remote Sensing, 2018
https://prowdiy.github.io/weihe.github.io/code/TV_RSNMF_public.zip
W. He, H. Zhang and L. Zhang, "Total Variation Regularized Reweighted Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing," in IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 7, pp. 3909-3921, July 2017, doi: 10.1109/TGRS.2017.2683719.
USGS spectral library version 7 for synthetic data generation https://pubs.er.usgs.gov/publication/ds1035 https://crustal.usgs.gov/speclab/QueryAll07a.php
Hyperspectral cube and area classification file from IEEE Data Fusion Contest 2018 https://site.ieee.org/france-grss/2018/01/16/data-fusion-contest-2018-contest-open/ https://hyperspectral.ee.uh.edu/?page_id=1075 Data download link: http://hyperspectral.ee.uh.edu/QZ23es1aMPH/2018IEEE/phase2.zip for default usage extract to Benchmark/phase2 folder. After extraction Benchmar/pahse2 should contain "2018IEEE_Contest" folder and its contents
Run dataset.py to generate the benchmark datasets. To get the parameters run
python3 dataset.py -h
No parameters are required as defaults are provided. Only the manual download of ieee dataset is needed
For testing purposes algorithms are run using dummy information using:
python3 algorithms.py
To run the created benchmark dataset through given algorithms use benchmark.py
python3 benchmark.py
Default parameters are given. Paths and other parameters can be changed in settings.py file