## 5 Conclusions  
To tackle the problem of feature confusion, which encompasses both the confusion between foreground features and background features and the confusion between foreground features of different classes, in optical remote sensing few-shot object detection (FSOD), we propose a new two-stage FSOD framework based on transfer learning via pseudo-sample generation and feature enhancement called PSGFE, including a pseudo-sample generation module (PSGM) and feature enhancement module (FEM). PSGM mines potential foreground instances from unlabeled backgrounds and generates pseudo-samples, addressing the confusion between background features and foreground features. FEM enhances ROI features, addressing the confusion among foreground features of different classes. The experiments confirmed the efficacy of PSGM and FEM in improving FSOD performance in optical remote sensing images. Despite the effectiveness of our method, it has certain limitations. One significant limitation is the degradation in base class performance during fine-tuning, as the model \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Baseline**} & \multirow{2}{*}{**PSGM**} & \multirow{2}{*}{**FEM**} & \multicolumn{3}{c}{**mAP50**} \\ \cline{3-5}  & & & **10-Shot** & **20-Shot** \\ \hline ✓ & \(\times\) & \(\times\) & 24.9 & 29.2 \\ ✓ & ✓ & \(\times\) & 26.3 & 32.8 \\ ✓ & \(\times\) & ✓ & 25.6 & 31.9 \\ ✓ & ✓ & ✓ & **27.8** & **33.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment of the main components of PSGFE. The bold parts in the tables indicate the best results under each setting. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Branch 1** & **Branch 2** & **Branch 3** & **mAP50** \\ \hline Case1 & ✓ & \(\times\) & \(\times\) & 30.7 \\ Case2 & ✓ & ✓ & \(\times\) & 31.6 \\ Case3 & ✓ & \(\times\) & ✓ & 31.3 \\ Case4 & ✓ & ✓ & ✓ & **31.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiment of the FEM position. The bold parts in the tables indicate the best results under each setting. tends to forget previously learned knowledge. Additionally, challenges arise in detecting objects with arbitrary orientations, which can reduce localization performance and classification accuracy. Furthermore, our method increases computational overhead. In future research, we will focus on addressing these three areas: (1) mitigating the forgetting issue by developing a more robust feature embedding that preserves base-class knowledge while accommodating novel classes, (2) exploring rotation-invariant feature extraction techniques to enhance the detection of objects with arbitrary orientations in remote sensing applications, and (3) investigating module lightening to reduce time complexity. Conceptualization, Z.H., D.C. and C.Z. ; methodology, Z.H., D.C. and C.Z. ; software, Z.H. ; validation, D.C. and C.Z. ; formal analysis, Z.H., D.C. and C.Z. ; writing--original draft, Z.H. ; writing--review & editing, Z.H., D.C. and C.Z. ; visualization, Z.H. ; supervision, D.C.; funding acquisition, D.C. and C.Z. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Natural Science Foundation of Guangxi Province, China (2025 GXNSFAA069540) and the Research Capacity Improvement Project of Young Researcher (No. 2024 KY0017). Not applicable. Publicly available datasets were analyzed in this study. This data can be found here: RSOD dataset at [[https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-](https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-)]([https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-](https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-))(accessed on 1 March 2025) and the DIOR dataset at [[https://github.com/JiekaiLab/dior](https://github.com/JiekaiLab/dior)]([https://github.com/JiekaiLab/dior](https://github.com/JiekaiLab/dior)) (accessed on 1 March 2025). The authors thank all anonymous reviewers for their constructive comments and also thank all editors for their careful proofreading.