Image Augmentation for Satellite Images
Oluwadara Adedeji, Peter Owoade, Opeyemi Ajayi, Olayiwola Arowolo

TL;DR
This paper investigates the use of GAN-based image augmentation to enhance the performance of land cover classification models on satellite imagery, demonstrating that combining GAN-generated images with geometric augmentation improves model generalization.
Contribution
The study introduces a method of augmenting satellite image datasets with GAN-generated images, showing that this approach enhances classification performance.
Findings
GAN augmentation improves model accuracy
Combining geometric and GAN augmentation yields better results
GAN architecture choice has minimal impact on performance
Abstract
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.
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Taxonomy
TopicsRemote-Sensing Image Classification · Remote Sensing and LiDAR Applications · Remote Sensing in Agriculture
Methods*Communicated@Fast*How Do I Communicate to Expedia? · HuMan(Expedia)||How do I get a human at Expedia? · Batch Normalization · Convolution · Deep Convolutional GAN
