# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 3 Experiment and Analysis
### Instance Segmentation Results on NWPU VHR-10  
The experiments follow the same several most popular IS algorithms as in Section 3.5, and the training hyperparameters are configured with the same default values as in the experiments. Different from the SSDD, NWPU-VHR-10 is a multicategory dataset that includes 10 categories, including airplane (AI), baseball diamond (BD), ground track field (GTF), vehicle (VC), ship (SH), tennis court (TC), harbor (HB), storage tank (ST), basketball court (BC), and bridge (BR). Therefore, we conducted experimental evaluations for each category. The results are shown in Tables 7-9. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **AP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **APs** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** \\ \hline Mask-RCNN [21] & 66.4 & 89.2 & 73.8 & 50.9 & 64.2 & 75.3 \\ \hline MS-RCNN [52] & 67.5 & 89.7 & 74.9 & 51.0 & 65.3 & 76.0 \\ \hline CM-RCNN [19] & 66.6 & 89.7 & 65.0 & 37.3 & 58.5 & 69.2 \\ \hline HTC [20] & 67.7 & 90.5 & 74.2 & 53.7 & 65.7 & 68.9 \\ \hline SC-Net [53] & 68.4 & 92.4 & 74.6 & 53.5 & 66.7 & 76.6 \\ \hline Insta-Boost [54] & 68.3 & 92.0 & 81.4 & 67.3 & 69.2 & 68.3 \\ \hline QCIS-Net & 73.3 & 93.0 & 75.7 & 56.6 & 67.2 & 79.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: IS on the NWPU-VHR-10 test set. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **AI** & **BD** & **GTF** & **VC** & **SH** & **TC** & **HB** & **ST** & **BC** & **BR** \\ \hline Mask-RCNN [21] & 48.5 & 80.5 & 94.2 & 54.4 & 54.2 & 79.2 & 53.2 & 81.8 & 77.4 & 37.7 \\ \hline MS-RCNN [52] & 49.5 & 83.3 & 94.9 & 54.4 & 57.5 & 79.7 & 54.6 & 82.6 & 79.5 & 38.7 \\ \hline CM-RCNN [19] & 48.9 & 80.5 & 95.8 & 54.7 & 55.9 & 78.7 & 53.2 & 82.6 & 81.5 & 34.0 \\ \hline HTC [20] & 50.2 & 83.6 & 95.4 & 54.7 & 54.5 & 82.3 & 54.4 & 82.7 & 82.2 & 36.6 \\ \hline SC-Net [53] & 52.5 & 84.0 & 95.0 & 55.5 & 59.4 & 82.6 & 54.7 & 82.6 & 82.5 & 36.0 \\ \hline Insta-Boost [54] & 50.8 & 84.0 & 94.2 & 56.5 & 56.2 & 83.3 & 53.7 & 84.0 & 81.5 & 37.0 \\ \hline QCIS-Net & 56.5 & 86.5 & 96.0 & 63.2 & 63.2 & 83.8 & 62.0 & 83.6 & 84.6 & 47.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classwise IS results on the NWPU-VHR-10 test set. Figure 8: Qualitative experimental results on the SSDD. (**a**) Shows the ground-truth annotations of the objects; (**b–g**) Shows the results of the other methods, Cascade Mask-RCNN, Mask-RCNN, MS-RCNN, HTC, SC-Net, and Insta-Boost, respectively; (**h**  
From Table 7, QCIS-Net achieves an AP score of 73.3% on the NWPU-VHR-10 test dataset for IS tasks. This score is 6.9% higher than the classical Mask-RCNN, indicating the network's effectiveness in IS of optical images. Compared to more advanced IS methods, such as HTC, SC-Net, and Insta-Boost, QCIS-Net also demonstrates superior performance, further highlighting its advantages in optical image IS tasks. Overall, our QCIS-Net achieves excellent results in IS tasks for optical images. Tables 8 and 9 report the IS and object-detection results of QCIS-Net on the optical dataset for each category. In terms of IS, our method achieves the best results in all categories except for storage tanks (which is only 0.4% lower than the optimal value), indicating QCIS-Net's effectiveness in extracting accurate masks for objects in optical images. In terms of object detection, QCIS-Net achieves the best results in all categories, showcasing its outstanding performance. Based on the experimental results from Tables 7-9, it is evident that our QCIS-Net excels in both IS and object-detection tasks for optical images. Figure 9 shows the results of QCIS-Net and the compared methods on the NWPU-VHR-10 dataset. QCIS-Net accurately segments the instance masks of the objects without apparent over- or under-segmentation phenomena.