HySUPP

An open-source HyperSpectral Unmixing Python Package

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Introduction

HySUPP is an open-source Python toolbox for hyperspectral unmixing practitioners.

HySUPP key numbers

• 3 unmixing categories
• 20 unmixing methods
• 4 metrics
• 3 simulated datasets (located under ./data/)
• 1 real-world dataset (located under ./data/)

License

HySUPP is distributed under MIT license.

Citing HySUPP

Rasti, B., Zouaoui, A., Mairal, J., & Chanussot, J. (2023). Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package. ArXiv. /abs/2308.09375

Installation

Using conda

We recommend using a conda virtual Python environment to install HySUPP.

In the following steps we will use conda to handle the Python distribution and pip to install the required Python packages. If you do not have conda, please install it using Miniconda.

conda create --name hysupp python=3.10

Activate the new conda environment to install the Python packages.

conda activate hysupp

Clone the Github repository.

git clone [email protected]:BehnoodRasti/HySUPP.git

Change directory and install the required Python packages.

cd HySUPP && pip install -r requirements.txt

If you encounter any issue when installing spams, we recommend reading the Installation section here. For windows users, we suggest removing line 10 in the requirements.txt (spams==2.6.5.4), and after installing the requirements, install spams using pip install spams-bin.

Getting started

This toolbox uses MLXP to manage multiple experiments built on top of hydra.

There are a few required parameters to define in order to run an experiment:

• mode: unmixing mode
• data: hyperspectral unmixing dataset
• model: unmixing model
• noise.SNR: input SNR (optional)

An example of a corresponding command line is simply:

python unmixing.py mode=semi data=DC1 model=SUnCNN projection=True

Data

Data format

Datasets consist in a dedicated .mat file containing the following keys:

• Y: original hyperspectral image (dimension L x N)
• E: ground truth endmembers (dimension L x p)
• A: ground truth abundances (dimension p x N)
• H: HSI number of rows
• W: HSI number of columns
• p: number of endmembers
• L: number of channels
• N: number of pixels (N == H*W)

For sparse unmixing, a dictionary D containing M atoms is required.

• D: endmembers library (dimension L x M)
• M: number of atoms

We provide a utility script to turn any existing datasets composed of separated files to fit the required format used throughout the toolbox (See utils/bundle_data.py).

Parameter Tuning

Fine Tuning

You may need to fine-tune the models' parameters for your application. Every method has a dedicated .yaml file located at config/model, which indicates the relevant parameters you can use for fine-tuning. For instance, for SUnCNN, the parameters are indicated in config/model/SUnCNN.yaml, and we can change the number of iterations and the input of the CNN with the following line.

python unmixing.py mode=semi data=DC1 model=SUnCNN projection=True model.niters=8000 model.noisy_input=False noise.SNR=30