## 1 Introduction  
Rapid advancement in the field of remote sensing technology has substantially increased the volume and fidelity of optical remote sensing images. Accurate analysis and interpretation of these images are crucial tasks in geographic sciences and play important roles in areas such as environmental surveillance [1], resource reconnaissance [2], and calamity detection [3]. Deep learning-based object detection is an important technique for analyzing and understanding optical remote sensing images [4, 5], relying on large-scale fully annotated data sets for training. However, the dynamic nature of the geographical environment means that novel classes may emerge in remote sensing images that were not previously encountered. Manually annotating these novel classes is not only costly but also time consuming. Moreover, with limited annotated training samples, traditional detectors are prone to overfitting, severely weakening their generalization ability. Consequently, demand for few-shot object detection (FSOD) is increasing, aiming to leverage prior knowledge acquired from a richly annotated base class dataset and a limited number of training samples from novel classes to accurately classify and locate both base and novel objects in images [6; 7; 8]. Its framework generally involves a two-stage training process: training on a huge labeled dataset of base classes, followed by fine-tuning on a balanced set containing only a few annotated instances. Although FSOD has shown promising results in natural scene datasets [9; 10; 11] with limited scale variation, optical remote sensing images present unique challenges due to their complex backgrounds. These challenges differ significantly from those encountered in natural scene images in the following aspects [12]: large variability in scale is observed across spatial resolutions, with diverse scales appearing both between different classes and within individual classes [13; 14]; and increased inter-class similarity and intra-class variability [15; 16]. These factors exacerbate the challenge of accurately recognizing novel class features, increasing the likelihood of misclassification between novel and base class instances, and ultimately diminishing the robustness and overall effectiveness of object detectors. Moreover, FSOD also encounters the following challenge: it is hard to distinguish between background features and foreground features, especially in the novel class [17; 18]. In the initial training phase, only base class instances are annotated, while novel class instances are treated as background. In the fine-tuning phase, only a subset of instances is annotated. This will lead the model to misinterpret the features of the foreground, particularly those of the novel class foreground, as background features, impairing its ability to differentiate between the real background and foreground in subsequent detection phases. This issue can be attributed to feature confusion, whether between background features and foreground features or among different foreground classes, which significantly impairs the model performance in classification and localization. To address the issue of feature confusion, some meta-learning methods [19; 20] aggregate the features of support-query sample pairs to make the learned feature space more discriminative. FSCE [21] and MSOCL [22] conduct contrastive learning at different scales to learn multi-scale features, thereby facilitating the distinction between foreground instances and the background. Although these methods help alleviate feature confusion to some extent, they have certain limitations. Meta-learning methods require the construction of support-query sample pairs, making them operationally complex. Contrastive learning methods are susceptible to interference from potential foreground instances in the background, which are mistakenly treated as negative samples, thus hindering the learning of class-specific features.