# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 3 Experiment and Analysis
### Ablation Experiments  
We performed ablation experiments where certain components of the algorithm were removed or replaced to measure the impact of these components on the overall performance. To verify the performance of the Swin Transformer for the network, we replaced the backbone of the network with the mainstream ResNet101 for comparison and designed the following ablation experiments. In addition, we also conducted experiments on the use of transfer learning. The results are shown in Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Backbone** & **AP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** \\ \hline \multirow{2}{*}{NWPU-VHR-10 [26]} & ResNet101 & 67.3 & 88.5 & 77.1 & 53.8 & 56.1 & 73.9 \\ \cline{2-7}  & Swin Transformer & 73.3 & 93.0 & 75.7 & 56.6 & 67.2 & 79.8 \\ \hline \multirow{2}{*}{SSDD [51]} & ResNet101 & 74.2 & 97.1 & 92.9 & 72.2 & 80.5 & 85.0 \\ \cline{2-7}  & Swin Transformer & 75.2 & 98.7 & 92.6 & 73.1 & 81.8 & 75.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effect of the EEF module.  
As shown in Table 1, compared to the stacked convolutional layers, the Swin Transformer as the backbone achieved a 6.0% AP improvement over ResNet-101 on the NWPU-VHR-10 dataset and a 1.0% AP improvement on the SSDD. This indicates that this attention-based backbone network has good adaptability to RS images and can effectively extract the features of the objects in the input images. Table 2 shows that transfer learning on the MS COCO dataset significantly improved the IS performance of QCIS-Net, which is due to the difficulty of training the cascade structure of QCIS-Net itself. The use of transfer learning based on large datasets allows better initialization of the network, which is beneficial for fine-tuning the optical and SAR image datasets.  
From Table 3, we conducted an ablation study on the number of cascade stages in the MSCT head in datasets. Without any cascading stages, the network's segmentation and detection performance unsurprisingly achieved the worst results. As the number of cascade stages in the MSCT head increased, both the segmentation and detection performance of the network improved. However, when the number of cascade stages became too large, the performance improvement was not significant. When we chose 4 or 6 as the number of cascade stages, the performance was good, and the difference was insignificant. Therefore, we chose 6 as the value of cascade number for optical images, and for SAR images, we chose 4 as the value.  
From Table 4, we experimented with different numbers of object queries. Increasing the number of object queries achieved higher AP values in both datasets. As for the results in the SSDD, the number of 300 achieves a salient AP performance compared to the rest of the situations. In the counterpart results for the NWPU-VHR-10 dataset, the relative increase in AP values was not quite so dramatic. However, increasing the number of object queries can make the model training extremely time-consuming.