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. The paper proposes a region-based convolutional neural network to detect aircrafts. To enhance the learning ability of the network, a multi-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.
Aircraft Detection in Remote Sensing Images Based on Deep Convolutional Neural Network
01.12.2017
196191 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Ship object detection in remote sensing images using convolutional neural networks
British Library Online Contents | 2017
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