3DGAN-ViT

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Here is the code developed for the paper "A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples" puplished in International Journal of Applied Earth Observation and Geoinformation.

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# 3DGAN-ViT

#### Here is the code developed for the paper "A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples" puplished in International Journal of Applied Earth Observation and Geoinformation.

![TwitterJAG](https://user-images.githubusercontent.com/22929034/200546384-a12ab473-6e12-41e1-bd1c-94fc1d86d89d.png)

Cite our paper as:

@article{JAMALI2022103095,
title = {A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {115},
pages = {103095},
year = {2022},
issn = {1569-8432},
doi = {https://doi.org/10.1016/j.jag.2022.103095},
url = {https://www.sciencedirect.com/science/article/pii/S1569843222002837},
author = {Ali Jamali and Masoud Mahdianpari and Fariba Mohammadimanesh and Saeid Homayouni},
keywords = {Generative adversarial network, Convolutional neural network, Wetland classification, New Brunswick, Vision Transformer (ViT), Deep learning},
}

### Ali Jamali, Masoud Mahdianpari, Fariba Mohammadimanesh, Saeid Homayouni, A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples, International Journal of Applied Earth Observation and Geoinformation, Volume 115, 2022, 103095, ISSN 1569-8432, https://doi.org/10.1016/j.jag.2022.103095.

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