This study proposes the use of generative models (GANs) for augmenting the EuroSAT dataset for the Land Use and Land Cover (LULC) Classification task. We used DCGAN and WGAN-GP to generate images for each class in the dataset. We then explored the effect of augmenting the original dataset by about 10% in each case on model performance. The choice of GAN architecture seems to have no apparent effect on the model performance. However, a combination of geometric augmentation and GAN-generated images improved baseline results. Our study shows that GANs augmentation can improve the generalizability of deep classification models on satellite images.
@article{arxiv.2207.14580,
title = {Image Augmentation for Satellite Images},
author = {Oluwadara Adedeji and Peter Owoade and Opeyemi Ajayi and Olayiwola Arowolo},
journal= {arXiv preprint arXiv:2207.14580},
year = {2022}
}
Comments
14 pages, 4 figures, 6 tables. Research project for Introduction to Deep Learning (11785) at Carnegie Mellon University