Discriminative Prototype Learning for Few-Shot Object Detection in Remote-Sensing Images

Few-shot object detection (FSOD) in remote-sensing images (RSIs), which aims to detect never-seen objects with few training samples, has attracted wide attention. Some recent works leverage meta-learning to tackle this challenging task and achieve promising performance. However, information attenuation during feature extraction and simple prototype representation hamper further improvement in detecting novel classes. In this article, we propose a novel meta-learning-based FSOD approach named DPL-Net. Specifically, within the meta-learning-based framework, performing role-specific feature extraction in query and support branches, DPL-Net adopts a fine-grained information fusion (FIF) module to capture scale-aware information within query regions of interest (RoIs) and a multifrequency information enhancement (MIE) module to retain the spectral information of support samples. Moreover, considering the variability of remote-sensing objects, a discriminative prototype learning (DPL) strategy is developed to rectify the ambiguous distribution of support samples for more representative class-aware prototypes (CPs). Experiments on two benchmark datasets (NWPU VHR-10 and DIOR) demonstrate that our method effectively improves the performance of meta-learning in detecting RSIs with limited training data.