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.
翻译:这项研究建议使用基因模型来扩大欧洲卫星系统的数据集,用于土地利用和土地覆盖(LULC)分类任务。我们利用DCGAN和WGAN-GP为数据集中的每个类别生成图像。然后我们探讨了在模型性能方面将原始数据集增加大约10%的影响。选择GAN结构似乎对模型性能没有明显的影响。然而,将几何增强和GAN生成的图像结合起来可以改善基线结果。我们的研究显示,GANs增强可以改善卫星图像深度分类模型的可概括性。