enhanced_scott

FAST FUSION OF HYPERSPECTRAL AND MULTISPECTRAL IMAGES : A TUCKER APPROXIMATION APPROACH

This software reproduces the results from the following:

@unpublished{prevost:hal-03617759,
  TITLE = ,
  AUTHOR = {Pr{\'e}vost, Cl{\'e}mence and Chainais, Pierre and Boyer, Remy},
  URL = {https://hal.archives-ouvertes.fr/hal-03617759},
  NOTE = {working paper or preprint},
  YEAR = {2022},
  MONTH = Mar,
  KEYWORDS = {hyperspectral super-resolution ; data fusion ; low-rank tensor factorizations ; recovery ; least-squares problem},
  PDF = {https://hal.archives-ouvertes.fr/hal-03617759/file/icip.pdf},
  HAL_ID = {hal-03617759},
  HAL_VERSION = {v1},
}

Link to the project

Content

/metrics : contains the metrics used to assess the quality of the reconstruction
/src : contains helpful files to run the demos

Minimal requirements

In order to run the demo files, you will need to:

Download and install Tensorlab 3.0: https://www.tensorlab.net
Download the dataset from the AVIRIS website: https://aviris.jpl.nasa.gov/dataportal/

Please quote the corresponding paper if you decide to use these codes.

## How it works

This software reproduces the figures and tables contained in the paper. You can play with the two datasets.

### Generate coupled tensor model

Real datasets are used. First, the HSI and MSI are generated following Wald’s protocol. Then, white Gaussian noise is added to the observations.

### Run algorithms

In fusion_isabella.m and fusion_lockwood.m, we showcase the performance of:

The proposed algorithm, with [2,2] and [4,4] block-patterns
SCOTT, for comparison

Plot results

The metrics and computation time are then displayed in a table. Slices of the reference and reconstructions are plotted in a figure. The color scale is generated according to the image’s spectral support.

Available demos

They are available in the /demos folder. The table below summarized what does what

Name	Content
`fusion_isabella.m`	Simulations for Isabella Lake dataset
`fusion_lockwood.m`	Simulations for Lockwood dataset
`choice_ranks.m`	plots R-SNR as a function of the multilinear ranks

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