Remote Sensing Aircraft Image Detection Based on Semi-Supervised Learning

Aiming at the existing remote sensing aircraft image detection methods based on deep learning, which require a large number of tagged data sets and a long training time, we propose a semi-supervised learning method based on generative adversarial networks (GANs). Two granularity deep-learning generative adversarial networks arc used to get the edge feature and deep semantic feature information. By combining these two discriminator networks of the GANs, we design the object detection model. The experiment shows that the proposed method has a faster training speed and less labeled dataset is needed during the training process.