Image Augmentation for Satellite Images
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 34 references that are in the map. First posted on arXiv 29 Jul 2022.
Builds on
16 of 34 references.
References · 16
- 2017Improved Training of Wasserstein GANscited by 2,235
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
- 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,496
- 2014Efficient Object Localization Using Convolutional Networkscited by 227
- 2020Early Stopping in Deep Networks: Double Descent and How to Eliminate itcited by 41
- 2015DeepSat - A Learning framework for Satellite Imagerycited by 38
- 2017Improving Deep Learning using Generic Data Augmentationcited by 36
- 2016MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classificationcited by 16
- 2016Active Deep Learning for Classification of Hyperspectral Imagescited by 11
- 2020Realistic River Image Synthesis using Deep Generative Adversarial Networkscited by 9
- 2021Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Studycited by 9
- 2019A Preliminary Study on Data Augmentation of Deep Learning for Image Classificationcited by 8
4 more
- 2017High-resolution Deep Convolutional Generative Adversarial Networkscited by 7
- 2020An Open-source Tool for Hyperspectral Image Augmentation in Tensorflowcited by 3
- 2019Cycle-Consistent Adversarial Networks for Realistic Pervasive Change Generation in Remote Sensing Imagerycited by 2
- 2020Semantic Segmentation of Medium-Resolution Satellite Imagery using Conditional Generative Adversarial Networkscited by 2
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.
3 papers.