# GAN-Based Object Removal in High-Resolution Satellite Images

arXiv: https://arxiv.org/abs/2301.11726 · HTML: https://mlsanity.com/paper/2301.11726/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 24 references that are in the map.
- First posted on arXiv 24 Jan 2023.

## Builds on (most cited first, 17 of 17)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [SinGAN: Learning a Generative Model from a Single Natural Image](https://mlsanity.com/paper/1905.01164/) (2019)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images](https://mlsanity.com/paper/1905.12886/) (2019)
- [Improved Techniques for Training Single-Image GANs](https://mlsanity.com/paper/2003.11512/) (2020)
- [Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks](https://mlsanity.com/paper/1609.06846/) (2016)
- [Manipulation Detection in Satellite Images Using Vision Transformer](https://mlsanity.com/paper/2105.06373/) (2021)
- [Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier](https://mlsanity.com/paper/1802.04881/) (2018)
- [Manipulation Detection in Satellite Images Using Deep Belief Networks](https://mlsanity.com/paper/2004.12441/) (2020)
- [Generative Autoregressive Ensembles for Satellite Imagery Manipulation Detection](https://mlsanity.com/paper/2010.03758/) (2020)

## Abstract (arXiv)

Satellite images often contain a significant level of sensitive data compared to ground-view images. That is why satellite images are more likely to be intentionally manipulated to hide specific objects and structures. GAN-based approaches have been employed to create forged images with two major problems: (i) adding a new object to the scene to hide a specific object or region may create unrealistic merging with surrounding areas; and (ii) using masks on color feature images has proven to be unsuccessful in GAN-based object removal. In this paper, we tackle the problem of object removal in high-resolution satellite images given a limited number of training data. Furthermore, we take advantage of conditional GANs (CGANs) to collect perhaps the first GAN-based forged satellite image data set. All forged instances were manipulated via CGANs trained by Canny Feature Images for object removal. As part of our experiments, we demonstrate that distinguishing the collected forged images from authentic (original) images is highly challenging for fake image detector models.