An Aircraft Detection Framework Based on Reinforcement Learning and Convolutional Neural Networks in Remote Sensing Images

Aircraft detection has attracted increasing attention in the field of remote sensing image analysis. Complex background, illumination change and variations of aircraft kind and size in remote sensing images make the task challenging. In our work, we propose an effective aircraft detection framework based on reinforcement learning and a convolutional neural network (CNN) model. Aircraft in remote sensing images can be accurately and robustly located with the help of the searching mechanism that the candidate region is dynamically reduced to the correct location of aircraft, which is implemented through reinforcement learning. The detection framework overcomes the difficulties that the current detection methods based on reinforcement learning are only able to detect a fixed number of objects. Specifically, we adopt the restricted EdgeBoxes that generate the high-quality candidate boxes through the prior aircraft knowledge at first. Then, we train an intelligent detection agent through reinforcement learning and apprenticeship learning. The detection agent accurately locates the aircraft in the candidate boxes within several actions, and it even performs better than the greed strategy in apprenticeship learning. During the final detection step, we carefully design the CNN model that predicts the probability that the localization result generated by the detection agent is an aircraft. Comparative experiments demonstrate the accuracy and efficiency of our aircraft detection framework.