  Object detection is one of the fundamental objectives in Applied Computer
Vision. In some of the applications, object detection becomes very challenging
such as in the case of satellite image processing. Satellite image processing
has remained the focus of researchers in domains of Precision Agriculture,
Climate Change, Disaster Management, etc. Therefore, object detection in
satellite imagery is one of the most researched problems in this domain. This
paper focuses on aircraft detection. in satellite imagery using deep learning
techniques. In this paper, we used YOLO deep learning framework for aircraft
detection. This method uses satellite images collected by different sources as
learning for the model to perform detection. Object detection in satellite
images is mostly complex because objects have many variations, types, poses,
sizes, complex and dense background. YOLO has some limitations for small size
objects (less than$\sim$32 pixels per object), therefore we upsample the
prediction grid to reduce the coarseness of the model and to accurately detect
the densely clustered objects. The improved model shows good accuracy and
performance on different unknown images having small, rotating, and dense
objects to meet the requirements in real-time.
