Few-shot fine-grained recognition in remote sensing ship images with global and local feature aggregation

Abstract

Remote sensing ship image detection methods have broad application prospects in areas such as maritime traffic and fisheries management. However, previous detection methods relied heavily on a large amount of accurately annotated training data. When the number of remote sensing ship targets is scarce, the detection performance of previous methods is unsatisfactory. To address this issue, this paper proposes a few-shot detection method based on global and local feature aggregation. Specifically, we introduce global and local feature aggregation. We aggregate query-image global and local features with support features. This encourages the model to learn invariant features under varying global feature conditions which enhances the model’s performance in training and inference. Building upon this, we propose combined feature aggregation, where query features are aggregated with all support features in the same batch, further reducing the confusion of target features caused by the imbalance between base-class samples and novel-class samples, improving the model’s learning effectiveness for novel classes. Additionally, we employ an adversarial autoencoder to reconstruct support features, enhancing the model’s generalization performance. Finally, the model underwent extensive experiments on the publicly available remote sensing ship dataset HRSC-2016. The results indicate that compared to the baseline model, our model achieved new state-of-the-art performance under various dataset settings. This model presented in this paper will provide new insights for few-shot detection work based on meta-learning.