# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 3 Experiment and Analysis
### Ship Segmentation Results of the SSDD
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **AP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** & **Time** \\ \hline Mask-RCNN [21] & 69.4 & 96.5 & 83.6 & 69.9 & 69.1 & 43.2 & 50.4 ms \\ \hline MS-RCNN [52] & 69.9 & 96.5 & 85.6 & 70.3 & 69.8 & 36.8 & 51.5 ms \\ \hline CM-RCNN [19] & 70.9 & 95.7 & 86.5 & 70.9 & 72.6 & 50.4 & 66.6 ms \\ \hline HTC [20] & 71.5 & 96.6 & 85.2 & 71.4 & 72.9 & 53.4 & 66.9 ms \\ \hline SC-Net [53] & 71.1 & 96.6 & 85.6 & 71.4 & 71.3 & 45.1 & 73.8 ms \\ \hline Insta-Boost [54] & 68.1 & 96.3 & 81.0 & 68.3 & 69.0 & 35.6 & 62.7 ms \\ \hline QCIS-Net & 79.1 & 98.7 & 93.6 & 78.1 & 85.1 & 80.1 & 55.3 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Object-detection results on the SSDD test set. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **AP\({}_{\text{Seg}}\)** & **AP\({}_{\text{box}}\)** & **AP\({}_{\text{Seg}}\)** & **AP\({}_{\text{box}}\)** \\ \hline
100 & 72.0 & 75.6 & 71.5 & 74.3 \\ \hline
200 & 72.6 & 76.5 & 71.7 & 74.5 \\ \hline
300 & 73.3 & 77.1 & 75.1 & 79.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The influence of the number of object queries. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **AP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** & **GFLOPs** \\ \hline Mask-RCNN [21] & 65.8 & 94.8 & 82.1 & 66.4 & 64.8 & 45.9 & 134.43 \\ \hline MS-RCNN [52] & 66.2 & 94.6 & 83.2 & 66.4 & 65.5 & 50.2 & 134.43 \\ \hline CM-RCNN [19] & 66.1 & 93.7 & 82.7 & 66.4 & 66.0 & 45.3 & 265.54 \\ \hline HTC [20] & 66.4 & 94.6 & 83.3 & 66.7 & 65.9 & 60.7 & 268.13 \\ \hline SC-Net [53] & 67.0 & 95.6 & 83.9 & 67.1 & 67.5 & 46.1 & 348.80 \\ \hline Insta-Boost [54] & 64.4 & 94.3 & 80.6 & 64.6 & 64.1 & 40.2 & 134.43 \\ \hline QCIS-Net & 75.2 & 98.7 & 92.6 & 73.1 & 81.8 & 75.5 & 221.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: IS results on the SSDD test set. is SAR images of the offshore. Specifically, in the scene in the first column, there are unfavorable factors, such as the dense arrangement of ships and mutual occlusion, which leads to the common phenomenon of missed detections and false detections in the IS comparison method. However, our proposed QCIS-Net demonstrates the ability to detect and segment ships in this scene to a significant extent, with segmented masks closely resembling the ground truth. This demonstrates the powerful detection and segmentation performance of QCIS-Net in SAR images. In the second column, the background contains objects that resemble ship appearances, making them prone to be easily and incorrectly detected as ships. In this scene, the comparison methods have exhibited varying degrees of missed detections and false detections. However, QCIS-Net effectively discriminates ships from the background, avoiding false detections. In the third column, although all comparison methods are capable of segmenting ships in the images, the obtained instance masks differ significantly from the ground truth. QCIS-Net overcomes the drawback of low mask quality and accurately detects object ships, verifying the superiority of our network. The fourth column reflects the performance of various algorithms on offshore SAR images, where the proportion of pixels occupied by the object is small, and there are many speckle noises, posing challenges for detection and segmentation. The proposed QCIS-Net can accurately detect and segment ships in the image, further proving its performance in SAR image IS. Overall, the proposed QCIS-Net exhibits good performance in SAR image IS and object-detection tasks, capable of handling various challenging scenarios, such as dense ship arrangements, occlusions, and a small proportion of object-occupied pixels.