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Over the recent years, object detection in satellite imagery has become a crucial task in remote sensing applications. Specifically, the detection of aircraft is critical for military scenarios. Numerous computer vision techniques have been applied for this problem, however, robust and efficient detection of aircraft in satellite imagery poses several challenges, such as, variance of color, size, aspect ratio and orientation of aircraft and complex backgrounds. In this paper, we provide an in-depth review of deep learning methods for object detection applied to aircraft detection including both conventional and state-of-the-art techniques. We also provide a comprehensive experimental and comparative analysis of deep learning-based object detectors including Region-based CNN (RCNN), Fast RCNN, Faster RCNN and You Only Look Once (YOLO) encapsulating the most cited feature extraction networks including Alexnet …