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Object detection and classification for aircraft are the most important tasksin the satellite image analysis. The success of modern detection andclassification 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 onmilitary action and oper- ation. Considering the characteristics of satelliteimagery, this paper attempts to provide a framework of the simulated andunsupervised methodology without any additional su- pervision or physicalassumptions. Finally, the qualitative and quantitative analysis revealed apotential to replenish insufficient data for machine learning platform forsatellite image analysis.

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