bLL1_NBTD

Software for nonnegative block-term decomposition with the beta-divergence:

This repository contains the software for joint HSR and unmixing using LL1 decomposition.

This MATLAB software reproduces the results from the following paper:

@unpublished{prevost:hal-03831661,
  TITLE = ,
  AUTHOR = {Pr{\'e}vost, Cl{\'e}mence and Leplat, Valentin},
  URL = {https://hal.archives-ouvertes.fr/hal-03831661},
  NOTE = {working paper or preprint},
  YEAR = {2022},
  MONTH = Oct,
  KEYWORDS = {Nonnegative tensor factorization ; block-term decomposition ; beta-divergence ; blind spectral unmixing ; hyperspectral super-resolution},
  PDF = {https://hal.archives-ouvertes.fr/hal-03831661/file/Paper_LongVersion.pdf},
  HAL_ID = {hal-03831661},
  HAL_VERSION = {v1},
}

Acknowledgements

The baseline algorithms used in the manuscript are courtesy of their respective authors.

Content

/data : contains synthetic datasets and reference endmembers for the Samson and Jasper Ridge dataset.
/demos : contains demo files that produce tables and figures (including demo.m).
main.m : codes that allow to run desired demos.
/src : contains helpful files to run the demos.

Minimal requirements

In order to run the demos, you will need to add to your MATLAB path:

Tensorlab 3.0: https://www.tensorlab.net

Additionnally, you will need:

The HSMS data fusion toolbox: https://openremotesensing.net/knowledgebase/hyperspectral-and-multispectral-data-fusion/
The tensor HSR toolbox: https://drive.google.com/file/d/1dwLwwNTseMGIGhxS432BC3PREkJSMlo7/view?usp=share_link

Demo file

A demo with minimal requirements is available. To proceed, please type “1” when running the main.m file.

## Tuning the parameters

There are several parameters that you can choose:

The noise type: Gaussian, Poisson or Gamma
The ranks Lr of the decomposition
The number of trials
The number of iterations and the threshold for the proposed approach.

For benchmarked approaches, the parameters have been tuned according to the original works.

## Reproduce figures from the paper

To do so, you need to run the main.m file. Here, a menu is available and allows you to choose which figure or table you want to generate. Each number in the table below corresponds to a set of figures.

Number	Content
1	Demo with minimal requirements
2	Benchmarking on synthetic dataset
3	Benchmarking on semi-real dataset
4	Benchmarking on semi-real dataset with unknown operators