Aircraft type recognition plays an important role in remote sensing image interpretation. Traditional methods suffer from bad generalization performance, while deep learning methods require large amounts of data with type labels, which are quite expensive and time-consuming to obtain. To overcome the aforementioned problems, in this paper, we propose an aircraft type recognition framework based on conditional generative adversarial networks (GANs). First, we design a new method to precisely detect aircrafts’ keypoints, which are used to generate aircraft masks and locate the positions of the aircrafts. Second, a conditional GAN with a region of interest (ROI)-weighted loss function is trained on unlabeled aircraft images and their corresponding masks. Third, an ROI feature extraction method is carefully designed to extract multi-scale features from the GAN in the regions of aircrafts. After that, a linear support vector machine (SVM) classifier is adopted to classify each sample using their features. Benefiting from the GAN, we can learn features which are strong enough to represent aircrafts based on a large unlabeled dataset. Additionally, the ROI-weighted loss function and the ROI feature extraction method make the features more related to the aircrafts rather than the background, which improves the quality of features and increases the recognition accuracy significantly. Thorough experiments were conducted on a challenging dataset, and the results prove the effectiveness of the proposed aircraft type recognition framework.

Aircraft type recognition of remote sensing images is critical both in civil and military applications. In this letter, we propose a novel landmark-based aircraft recognition method which is highly accurate and efficient. First, we propose a new idea to address the aircraft type recognition problem by aircraft's landmark detection. Its advantages are two folds. On the one hand, it needs fewer labeled data and alleviates the work of human annotation. On the other hand, a trained model has strong expansibility because it can be used for any type of aircraft that not contained in training data set without retraining. Then, we use a variant of a convolutional neural network called vanilla network for all landmarks regression at the same time. Therefore, it can avoid bad local minimum effectively by encoding the geometric constraints among landmarks implicitly. To handle aircrafts in myriads of poses, rotation jittering is used for data augmentation in preprocessing and multicrop fusion is used in postprocessing. Thus, an 80% reduction in error rate could be reached. Finally, we use the landmark template matching to recognize the aircraft. Our method shows a competitive performance both in accuracy and efficiency.