Few-Shot Object Detection in Satellite Imagery with Feature Fusion Pyramid and Adaptive Region Proposal Networks

Abstract

Object detection in satellite imagery presents unique challenges due to the wide variation in object sizes, shapes, and orientations, as well as the limited availability of labeled data for training models. Few-Shot Object Detection (FSOD) aims to address these challenges by enabling models to detect novel objects with only a few labeled examples. However, existing methods struggle to effectively capture multi-scale features and generate flexible region proposals, which are critical for accurate detection in complex aerial scenes. In this paper, we propose FFARPNet, a novel framework specifically designed for FSOD in satellite imagery. Our model introduces two key components: the Feature Fusion Pyramid Network (FFPN), which enhances multi-scale feature representation, and the Adaptive Region Proposal Network (ARPN), which dynamically adjusts region proposals to handle the diverse object scales and shapes found in aerial images. We evaluate FFARPNet on two challenging datasets, DIOR and NWPU VHR-10, and demonstrate significant improvements in detection accuracy across 3-shot, 5-shot, 10-shot, and 20-shot scenarios. Comparative analysis with state-of-the-art methods and ablation studies demonstrate the effectiveness of our proposed model and its core modules. The results highlight the robustness and generalization capability of our model, indicating its potential for remote sensing applications, particularly in scenarios with limited training data.