Domain Adaptive Generation of Aircraft on Satellite Imagery via Simulated and Unsupervised Learning
This addresses data scarcity for satellite image analysis, particularly for military applications, but appears incremental as it builds on existing unsupervised methods.
The paper tackled the problem of insufficient aircraft data for training object detection and classification models in satellite imagery by proposing a simulated and unsupervised learning framework, resulting in qualitative and quantitative evidence of its potential to replenish data for machine learning platforms.
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.