Aircraft Recognition Based on Landmark Detection in Remote Sensing Images

Abstract

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.