Abstract: Airplane detection in remote sensing images is a challenging task and researchers have been sparing no efforts in making breakthroughs in this topic. The methods based on artificial neural network are the main methods for airplane detection in remote sensing images. However, a large number of data must be annotated for training, which is time-consuming. This is one of the main bottlenecks to limit large-scale data to be effectively used. To address this problem, a generative adversarial networks (GAN)-based semi-supervised object detection method is proposed in this work. In airplane detection in remote sensing images, the method does not need to annotate all of the images, but only a small part of them, and then trains them together with a large number of unannotated data to achieve excellent detection performance. The proposed method combines a traditional detection network with a semi-supervised learning network based on GAN. During the adversarial training procedure, the generator learns the data distribution and generates fake samples, while the discriminator distinguishes between the true samples and the generated fake samples. At the same time, the discriminator is also trained on labeled data. Finally, the discriminator learns the decision boundary that not only separates labeled samples, but also parallels with the distribution boundary of the whole data. Experiments show that, with a large number of images, reducing the proportion of labeled data will significantly reduce the performance of the full-supervised learning method, while the semi-supervised learning method proposed in this work maintains the detection performance due to the use of unlabeled data.

Key words: semi-supervised learning, generative adversarial networks (GAN), object detection