Aircraft Detection in Remote Sensing Images Based On Deep Convolutional Neural Network

Aircraft detection in remote sensing images is always the research hotspot but a challenging task for the variations of aircraft type, pose, size and complex background. In the paper, we propose a region-based convolutional neural network to detect aircrafts. To enhance the learning ability of the network, a mult-resolution aircraft remote sensing dataset is collected from Google Earth. Then, the detection model is trained end to end by fine-tuning on the obtained dataset and realizes automatic aircraft recognition and positioning. Experiments show that the proposed method outperforms state-of-the-art method on the same dataset and the requirement for real-time can be satisfied simultaneously.