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DeepHyperX

A Python tool to perform deep learning experiments on various hyperspectral datasets.

https://www.onera.fr/en/research/information-processing-and-systems-domain

https://www-obelix.irisa.fr/

Reference

This toolbox was used for our review paper in Geoscience and Remote Sensing Magazine :

N. Audebert, B. Le Saux and S. Lefevre, "Deep Learning for Classification of Hyperspectral Data: A Comparative Review," in IEEE Geoscience and Remote Sensing Magazine, vol. 7, no. 2, pp. 159-173, June 2019.

Bibtex format :

@article{8738045, author={N. {Audebert} and B. {Le Saux} and S. {Lefèvre}}, journal={IEEE Geoscience and Remote Sensing Magazine}, title={Deep Learning for Classification of Hyperspectral Data: A Comparative Review}, year={2019}, volume={7}, number={2}, pages={159-173}, doi={10.1109/MGRS.2019.2912563}, ISSN={2373-7468}, month={June},}

Requirements

This tool is compatible with Python 2.7 and Python 3.5+.

It is based on the PyTorch deep learning and GPU computing framework and use the Visdom visualization server.

Setup

The easiest way to install this code is to create a Python virtual environment and to install dependencies using: pip install -r requirements.txt

(on Windows you should use pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html)

Docker

Alternatively, it is possible to run the Docker image.

Grab the image using:

docker pull registry.gitlab.inria.fr/naudeber/deephyperx:preview

And then run the image using:

docker run -p 9999:8097 -ti --rm -v `pwd`:/workspace/DeepHyperX/ registry.gitlab.inria.fr/naudeber/deephyperx:preview

This command:

All data and products are stored in the current folder.

Users can build the Docker image locally using the Dockerfile using the command docker build ..

Hyperspectral datasets

Several public hyperspectral datasets are available on the UPV/EHU wiki. Users can download those beforehand or let the tool download them. The default dataset folder is ./Datasets/, although this can be modified at runtime using the --folder arg.

At this time, the tool automatically downloads the following public datasets:

The Data Fusion Contest 2018 hyperspectral dataset is also preconfigured, although users need to download it on the DASE website and store it in the dataset folder under DFC2018_HSI.

An example dataset folder has the following structure:

Datasets
├── Botswana
│   ├── Botswana_gt.mat
│   └── Botswana.mat
├── DFC2018_HSI
│   ├── 2018_IEEE_GRSS_DFC_GT_TR.tif
│   ├── 2018_IEEE_GRSS_DFC_HSI_TR
│   ├── 2018_IEEE_GRSS_DFC_HSI_TR.aux.xml
│   ├── 2018_IEEE_GRSS_DFC_HSI_TR.HDR
├── IndianPines
│   ├── Indian_pines_corrected.mat
│   ├── Indian_pines_gt.mat
├── KSC
│   ├── KSC_gt.mat
│   └── KSC.mat
├── PaviaC
│   ├── Pavia_gt.mat
│   └── Pavia.mat
└── PaviaU
    ├── PaviaU_gt.mat
    └── PaviaU.mat

Adding a new dataset

Adding a custom dataset can be done by modifying the custom_datasets.py file. Developers should add a new entry to the CUSTOM_DATASETS_CONFIG variable and define a specific data loader for their use case.

Models

Currently, this tool implements several SVM variants from the scikit-learn library and many state-of-the-art deep networks implemented in PyTorch.

Adding a new model

Adding a custom deep network can be done by modifying the models.py file. This implies creating a new class for the custom deep network and altering the get_model function.

Usage

Start a Visdom server: python -m visdom.server and go to http://localhost:8097 to see the visualizations (or http://localhost:9999 if you use Docker).

Then, run the script main.py.

The most useful arguments are:

There are more parameters that can be used to control more finely the behaviour of the tool. See python main.py -h for more information.

Examples:

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