# Image Augmentation for Satellite Images

arXiv: https://arxiv.org/abs/2207.14580 · HTML: https://mlsanity.com/paper/2207.14580/

## 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 (most cited first, 16 of 16)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Efficient Object Localization Using Convolutional Networks](https://mlsanity.com/paper/1411.4280/) (2014)
- [Early Stopping in Deep Networks: Double Descent and How to Eliminate it](https://mlsanity.com/paper/2007.10099/) (2020)
- [DeepSat - A Learning framework for Satellite Imagery](https://mlsanity.com/paper/1509.03602/) (2015)
- [Improving Deep Learning using Generic Data Augmentation](https://mlsanity.com/paper/1708.06020/) (2017)
- [MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification](https://mlsanity.com/paper/1612.08879/) (2016)
- [Active Deep Learning for Classification of Hyperspectral Images](https://mlsanity.com/paper/1611.10031/) (2016)
- [Realistic River Image Synthesis using Deep Generative Adversarial Networks](https://mlsanity.com/paper/2003.00826/) (2020)
- [Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study](https://mlsanity.com/paper/2110.02580/) (2021)
- [A Preliminary Study on Data Augmentation of Deep Learning for Image Classification](https://mlsanity.com/paper/1906.11887/) (2019)
- [High-resolution Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1711.06491/) (2017)
- [An Open-source Tool for Hyperspectral Image Augmentation in Tensorflow](https://mlsanity.com/paper/2003.13502/) (2020)
- [Cycle-Consistent Adversarial Networks for Realistic Pervasive Change Generation in Remote Sensing Imagery](https://mlsanity.com/paper/1911.12546/) (2019)
- [Semantic Segmentation of Medium-Resolution Satellite Imagery using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2012.03093/) (2020)

## Built on by (most cited first, 3 of 3)

- [Generate Your Own Scotland: Satellite Image Generation Conditioned on Maps](https://mlsanity.com/paper/2308.16648/) (2023)
- [Image augmentation with invertible networks in interactive satellite image change detection](https://mlsanity.com/paper/2510.18660/) (2025)
- [Efficient Geometry-Controlled High-Resolution Satellite Image Synthesis](https://mlsanity.com/paper/2605.04557/) (2026)

## 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.