## 4 Experiments
### Quantitative Results on DIOR Datasets  
To confirm the efficacy of PSGFE, we compare several related FSOD algorithms and conduct experiments: transfer-learning-based methods such as TFA [31], FSCE [21] and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Split** & & \multicolumn{2}{c}{**Novel Classes**} & \multicolumn{2}{c}{**Base Classes**} \\ \hline
1 & airplane & ground track field & expressway toll station & harbor & airport & rest \\
2 & golf course & tennis court & storage tank & dam & vehicle & rest \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Two different base/novel classes split setting for DIOR dataset. G-FSOD [50], which utilize balanced sets during the fine-tuning phase and optimize the RoI feature extractor through a specialized module to enhance adaptability to novel classes; and meta-learning-based methods such as RepMet [11], MPSR [30], FsDet [40] and P-CNN [39], which extract feature prototypes and perform feature aggregation to strengthen classification capabilities. MSOCL [22] and ST-FSOD [23] are the two best performing algorithms on the DIOR dataset, both based on transfer learning, and MSOCL [22] is our baseline. Our results, as shown in Table 2, achieved optimal performance in most cases, with the exception of a suboptimal result in the Split2 3-shot scenario, where the mAP50 of the novel class is 0.3 points lower than ST-FSOD [23]. The reason for this is that the data complexity of Split2 is relatively lower than that of Split1. FEM may focus excessively on certain local features, which can lead to overfitting of the model when there are only three training samples. For a more intuitive comparison, we select MSOCL [22] and ST-FSOD [23] for individual contrast. As illustrated in Figure 2a, in Split1, the mAP50 of our method for novel classes is on average 1.1 higher than that of the second-best method, and it improved by up to 1.3 under the 5-shot condition. Compared to our baseline MSOCL [22], our method achieved at least a 1.1 mAP50 enhancement, and in the 20-shot setting, it was further improved by 4.5 mAP50. In terms of percentage improvement, as shown in Figure 2b, our approach achieved at least a 2.7% increase over the second-best method, with the highest reaching 8.4%. Compared to the baseline, it has at least an improvement of 6.3% and, in the 20-shot configuration, it improved further by 15.4%. In Split2, our method on average surpassed the second-best method by 0.4 mAP50 and on average outperformed the baseline by 3.1 mAP50. Moreover, in all scenarios except for the 3-shot setting, it exceeded the second-best method, achieving an 11.8% improvement compared to the baseline. The results demonstrate that the approach of leveraging PSGM to mine potential foreground instances from complex backgrounds and generate high-quality pseudo-samples, combined with enhancing ROI features through FEM, is effective. PSGN effectively mitigates the confusion between background features and foreground features, while FEM effectively alleviates the confusion among features of different foreground instance classes. This validates the effectiveness of PSGFE in addressing the FSOD problem in remote sensing imagery.