Toward aircraft detection and fine-grained recognition from remote sensing images

The detection and recognition of aircraft on images is an important task in the field of optical remote sensing image interpretation. The challenges include the detection of arbitrary orientation targets and the fine-grained recognition of aircraft type. However, most related studies only pay attention to one of these because of the limitation of datasets, and an end-to-end network often cannot simultaneously solve these challenges well. Therefore, we propose a cascade framework for arbitrary-oriented and multitype aircraft detection in remote sensing images based on the convolutional neural network. A rotation-based detector that uses oriented boxes is designed to determine the position and direction of aircraft on remote sensing images. A fine-grained recognition subnetwork that adopts the ensemble learning and the Fisher discriminant regularization is designed to recognize the type of aircraft on images. The subnetwork is cascaded behind the rotation-based detector to achieve more accurate recognition. The mean average precision of this method reaches 83.7% on a challenging dataset, which is 2.4% to 8.2% higher than other advanced methods. Experiments demonstrated that the proposed method can greatly improve the accuracy of aircraft detection and recognition and that the method has good generalization performance. (c) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)