Satellite Image Representations for Quantum Classifiers

Code repository for the article "Satellite Image Representations for Quantum Classifiers" in the special issue of Datenbank-Spektrum "Data Management on Quantum Hardware".

Setup

We provide a Dockerfile for an easy setup. Clone the repository and, in the top-level directory, execute:

docker build -t sirfqc .

Then, start the container.

Data

The EuroSAT dataset can be downloaded here. The NWPU-RESISC45 dataset can be downloaded here.

Getting started

To train a model with default parameters (Data: EuroSAT AnnualCrop vs SeaLake, Transformation: VGG16+AE, Classifier: FVQC), simply execute:

python train.py

To get information and help on the parameters and possible arguments for the script, run:

python train.py -h

To train models for a one-versus-rest multiclass classification with default parameters (Data: EuroSAT, Transformation: VGG16+AE, Classifier: FVQC), execute:

python train_ovr.py

Confusion matrix as an example result for a one-versus-rest multiclass classification of the EuroSAT dataset with VGG16, autoencoder and FVQC.