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
2017-12-01
196191 byte
Conference paper
Electronic Resource
English
Ship object detection in remote sensing images using convolutional neural networks
British Library Online Contents | 2017
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