LL1_HSR_HU

Software for LL1-based hyperspectral super-resolution and blind unmixing with variable images

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

Copyright (c) 2021 Clémence Prévost, Ricardo Borsoi, Konstantin Usevich, José Bermudez, David Brie, Cédric Richard
Contact: clemence.prevost@univ-lorraine.fr

This MATLAB software reproduces the results from the following paper:

@article{prevost:hal-03158076,
  TITLE = ,
  AUTHOR = {Pr{\'e}vost, Cl{\'e}mence and Borsoi, Ricardo A and Usevich, Konstantin and Brie, David and Bermudez, Jos{\'e} C. M. and Richard, C{\'e}dric},
  URL = {https://hal.archives-ouvertes.fr/hal-03158076},
  JOURNAL = ,
  HAL_LOCAL_REFERENCE = {BioSiS},
  PUBLISHER = ,
  VOLUME = {15},
  NUMBER = {1},
  PAGES = {110-138},
  YEAR = {2022},
  MONTH = Jan,
  DOI = {10.1137/21M1409354},
  KEYWORDS = {Hyperspectral super-resolution ; spectral variability ; hyperspectral unmixing ; image fusion ; tensor decompositions},
  PDF = {https://hal.archives-ouvertes.fr/hal-03158076v2/file/LL1_SIAM_R2.pdf},
  HAL_ID = {hal-03158076},
  HAL_VERSION = {v2},
}

Acknowledgements

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

Content

• demo.m : demo file with minimal requirements.

• /data : contains synthetic datasets and initial factors.

• /demos : contains demo files that produce tables and figures (including demo.m).

• /figures : where the figures are saved.

• main.m : codes that allow to run desired demos.

• /metrics : contains the comparison metrics used to assess performance.

• /src : contains helpful files to run the demos.

Minimal requirements

In order to run the demo file and reproduce the figures, you will need to:

• Download and install Tensorlab 3.0: https://www.tensorlab.net

Demo file

A demo with minimal requirements is available. To proceed, please run the demo.m file.

### Load data

The synthetic datasets are available in the \data folders. The Ivanpah Playa and Lake Tahoe datasets are available online https://landsat.gsfc.nasa.gov.

## 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	Image fusion - Lake Tahoe dataset
2	Image fusion - Ivanpah Playa dataset
3	Image fusion - Indian Pines dataset
4	Unmixing - field-like synthetic dataset
5	Unmixing - synthetic dataset, non-id. NMF
6	Unmixing - Lake Tahoe dataset
7	Unmixing - Ivanpah Playa dataset