# Domain Adaptive Generation of Aircraft on Satellite Imagery via Simulated and Unsupervised Learning

arXiv: https://arxiv.org/abs/1806.03002 · HTML: https://mlsanity.com/paper/1806.03002/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 1 of 1)

- [Synthetic Data and Hierarchical Object Detection in Overhead Imagery](https://mlsanity.com/paper/2102.00103/) (2021)

## Abstract (arXiv)

Object detection and classification for aircraft are the most important tasks in the satellite image analysis. The success of modern detection and classification methods has been based on machine learning and deep learning. One of the key requirements for those learning processes is huge data to train. However, there is an insufficient portion of aircraft since the targets are on military action and oper- ation. Considering the characteristics of satellite imagery, this paper attempts to provide a framework of the simulated and unsupervised methodology without any additional su- pervision or physical assumptions. Finally, the qualitative and quantitative analysis revealed a potential to replenish insufficient data for machine learning platform for satellite image analysis.