# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 3 Experiment and Analysis
### Ship Segmentation Results of the SSDD  
To validate the performance of QCIS-Net, we compared it with several popular IS algorithms, including the Mask R-CNN [21], Mask Scoring R-CNN [52], Cascade Mask R-CNN [19], SC-Net [53], HTC [20], and Insta-Boost [54]. These algorithms include both \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Backbone** & **MS COCO** & **AP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** \\ \hline \multirow{3}{*}{NWPU-VHR-10 [26]} & \multirow{3}{*}{ResNet101} & \(\surd\) & 67.3 & 88.5 & 77.1 & 53.8 & 56.1 & 73.9 \\  & & & 53.4 & 75.0 & 58.1 & 36.4 & 52.3 & 66.7 \\ \cline{2-8}  & \multirow{2}{*}{SwinT-Large} & \(\surd\) & 73.3 & 93.0 & 75.7 & 56.6 & 67.2 & 79.8 \\  & & & 54.6 & 76.5 & 59.5 & 35.4 & 52.9 & 70.0 \\ \hline \multirow{3}{*}{SSDD [51]} & \multirow{3}{*}{ResNet101} & \(\surd\) & 74.2 & 97.1 & 92.9 & 72.2 & 80.5 & 85.0 \\  & & & 59.1 & 85.7 & 74.6 & 56.7 & 65.8 & 75.3 \\ \cline{1-1} \cline{2-8}  & \multirow{3}{*}{SwinT-Large} & \(\surd\) & 75.2 & 98.7 & 92.6 & 73.1 & 81.8 & 75.5 \\ \cline{1-1}  & & & 60.2 & 86.3 & 75.9 & 58.1 & 65.9 & 70.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effect of transfer learning on MS COCO. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**NWPU-VHR-10 [26]**} & \multicolumn{2}{c}{**SSDD [51]**} \\ \hline
**Stage** & **AP\({}_{\text{Seg}}\)** & **AP\({}_{\text{box}}\)** & **AP\({}_{\text{Seg}}\)** & **AP\({}_{\text{box}}\)** \\ \hline No cascade & 35.6 & 22.3 & 46.1 & 31.8 \\ \hline
2 & 63.2 & 64.1 & 65.6 & 68.0 \\ \hline
4 & 72.7 & 76.6 & 75.2 & 79.1 \\ \hline
6 & 73.3 & 77.1 & 75.1 & 79.1 \\ \hline
8 & 71.9 & 76.5 & 70.8 & 74.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The influence of stage numbers on the MSTC head. one-step and multistage detection methods. For both training and testing, we followed the default hyperparameters in MMDetection 2.0 [55], using ResNet-101 and FPN [27] as the backbone. Additionally, we use Giga floating point operations (GFLOPs) to estimate the model's complexity and computational requirements. The experimental results are presented in Tables 5 and 6. Tables 5 and 6 present the performance of QCIS-Net in IS and object detection. Compared to the classical Mask-RCNN algorithm, QCIS-Net achieves a 9.4% and 9.7% improvement in segmentation and detection AP, respectively, demonstrating the outstanding performance of the proposed method in SAR image-segmentation and detection tasks. Compared to other IS methods, such as MS-RCNN, HTC, and SC-Net, QCIS-Net also demonstrates certain advantages in segmentation and detection performance. This further highlights the powerful capabilities of our method in handling SAR image-segmentation and detection tasks. In summary, the proposed QCIS-Net exhibits excellent performance in SAR image IS and object-detection tasks due to the elaborated EFE module, MSCT head, joint loss function, and transfer-learning training scheme. It outperforms various methods used for comparison with an absolute advantage. Figure 8 visualizes the result of QCIS-Net compared to the other IS methods on the SSDD. The first to third columns are the SAR images of the inshore, and the fourth column