# Self-Attending Task Generative Adversarial Network for Realistic Satellite Image Creation

arXiv: https://arxiv.org/abs/2111.09463 · HTML: https://mlsanity.com/paper/2111.09463/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 18 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Learning from Simulated and Unsupervised Images through Adversarial Training](https://mlsanity.com/paper/1612.07828/) (2016)

## Built on by (most cited first, 2 of 2)

- [SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models](https://mlsanity.com/paper/2309.16812/) (2023)
- [Neural-based Compression Scheme for Solar Image Data](https://mlsanity.com/paper/2311.02855/) (2023)

## Abstract (arXiv)

We introduce a self-attending task generative adversarial network (SATGAN) and apply it to the problem of augmenting synthetic high contrast scientific imagery of resident space objects with realistic noise patterns and sensor characteristics learned from collected data. Augmenting these synthetic data is challenging due to the highly localized nature of semantic content in the data that must be preserved. Real collected images are used to train a network what a given class of sensor's images should look like. The trained network then acts as a filter on noiseless context images and outputs realistic-looking fakes with semantic content unaltered. The architecture is inspired by conditional GANs but is modified to include a task network that preserves semantic information through augmentation. Additionally, the architecture is shown to reduce instances of hallucinatory objects or obfuscation of semantic content in context images representing space observation scenes.