Detecting Fine-Grained Airplanes in SAR Images With Sparse Attention-Guided Pyramid and Class-Balanced Data Augmentation

Abstract

Airplane detection in synthetic aperture radar (SAR) images has drawn much attention owing to the success of deep learning methods. However, the development of fine-grained airplane detection in SAR images is still in a dilemma due to the small inter-class variance and the large intra-class variance in complex scenes with strong interference from the background. In addition, the class imbalance problem in multi-class fine-grained airplane recognition also significantly limits the direct application of general deep-learning-based airplane detectors. This paper proposes two effective methods to tackle the above two problems, respectively. First, we propose a sparse attention-guided fine-grained pyramid (SA-FP) module to simultaneously sample discriminative local features scattered in multi-scale layers and adaptively aggregate them with fine-grained attention to better classify subordinate-level airplanes with multiple scales. Second, a simple class-balanced copy-paste data augmentation (CC-DA) strategy, which randomly copies an airplane of one category and pastes it onto an image according to the class-wise probability, is proposed for class balance. Finally, extensive experiments on one public dataset and three representative deep-learning-based detection benchmarks are conducted to show the effectiveness and generalization of the two proposed methods. The combination of these two methods based on the Cascade R-CNN benchmark also won fifth place in fine-grained airplane detection in SAR images in the 2021 Gaofen challenge.