Image Augmentation for Satellite Images
This is an incremental improvement for satellite image analysis, addressing data scarcity in remote sensing tasks.
The study tackled the problem of improving Land Use and Land Cover classification on satellite images by using GANs for data augmentation, finding that combining geometric and GAN-generated images improved baseline results, though GAN architecture choice had no apparent effect.
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.