  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.
