# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 4 Results Show
However, shortcomings emerge, including its inability to discern coastal wetlands within the 6 m sea level and misclassifying Sai Wan Lake as coastal instead of constructed wetland. In contrast, the RegNet model (Figure 14d) demonstrates a more proficient handling of these issues. Despite slight misclassifications beyond the 6 m sea level near the Coloane Island Sea area, primarily within cloudy image regions possibly stemming from uneven original image quality [48], the model aptly categorizes Sai Van Lake and comprehensively captures coastal wetlands within the 6 m sea level, showcasing an interpretation surpassing the reference ResNet model. Figure 14i, showcasing CA-RegNet's segmentation, emerges with the most superior quality, rectifying challenges encountered by ResNet and further reducing misclassifications near the Coloane Island Sea area--a complication faced by RegNet, potentially due to uneven original image quality. The Inception-RegNet model largely offers accurate identifications, but image obfuscation from clouds and fog degrades the segmentation quality in certain sea regions [49]. However, models like Res2 Net, EfficientNet B7, and ConvNeXt, while proficient in identifying constructed wetlands on terrestrial zones, manifest considerable discrepancies, particularly within the inner sea and around the Coloane Island Sea region. The root causes might encompass uneven image quality or potential model overfitting, leading to performances lagging behind the benchmark ResNet model. The RegNet and CA-RegNet models surpass the ResNet model both in accuracy metrics and segmentation quality. The CA-RegNet model achieves an impressive OA of 0.9749, a Kappa coefficient of 0.9624, a mAcc of 0.9652 and a mIoU of 0.9517. Upon visual examination and analysis, its segmentation results ameliorate certain challenges present in both the ResNet and RegNet models. Both quantitative accuracy indices and visual interpretation collectively attest to the robustness and superior performance of the CA-RegNet model, particularly in segmenting coastal and constructed wetlands in Macau. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \begin{tabular}{c} **Coastal** \\ **Wetlands** \\ **UA** \\ \end{tabular} & \begin{tabular}{c} **Coastal** \\ **Wetlands** \\ **PA** \\ \end{tabular} & \begin{tabular}{c} **Coastal** \\ **Wetlands** \\ **F1-Score** \\ \end{tabular} & \begin{tabular}{c} **Constructed** \\ **Wetlands** \\ **UA** \\ \end{tabular} & \begin{tabular}{c} **Costructed** \\ **Wetlands** \\ **F1-Score** \\ \end{tabular} & \begin{tabular}{c} **Others** \\ **UA** \\ \end{tabular} & \
\begin{tabular}{c} **Others** \\ **PA** \\ \end{tabular} &
\begin{tabular}{c} **Others** \\ **UA** \\ \end{tabular} \\ \hline ResNet & 0.9782 & 0.9072 & 0.9447 & 0.9134 & 0.9649 & 0.9587 & 0.9563 & 0.9714 & 0.9809 \\ Res2 Net & 0.8542 & 0.9305 & 0.8909 & 0.9247 & 0.9481 & 0.9324 & 0.9707 & 0.8964 & 0.9420 \\ RegNet & 0.9887 & 0.9400 & 0.9277 & 0.9415 & 0.9826 & 0.9473 & 0.9839 & 0.9892 & 0.9649 \\ EfficientNet & & & & & & & & & \\ B7 & 0.9767 & 0.8444 & 0.9053 & 0.8099 & 0.9943 & 0.8901 & 0.9851 & 0.9422 & 0.9632 \\ ConvNeXt & 0.9325 & 0.8669 & 0.8988 & 0.9279 & 0.9295 & 0.9287 & 0.8794 & 0.8568 & 0.9167 \\ Inception-RegNet & 0.9627 & 0.9671 & 0.9637 & 0.9667 & 0.9484 & 0.9465 & 0.9611 & 0.9691 & 0.9673 \\ SE-RegNet & 0.9561 & 0.9751 & 0.9709 & 0.9664 & 0.9440 & 0.9443 & 0.9674 & 0.9707 & 0.9704 \\ CA-RegNet & 0.9481 & 0.9874 & 0.9674 & 0.9975 & 0.9419 & 0.9488 & 0.9812 & 0.9953 & 0.9891 \\ \hline \end{tabular}
\end{table} 
Table 1: The UA (Recall), PA (Precision) and F1-Score of each category. Figure 12: Normalized Matrix of each model. (**a**) Normalized Matrix of CA-RegNet; (**b**) Normalized Matrix of SE-RegNet; (**c**) Normalized Matrix of Inception-RegNet; (**d**) Normalized Matrix of RegNet; (**e**) Normalized Matrix of EfficientNet B7; (**f**) Normalized Matrix of ConvNeXt; (**g**) Normalized Matrix of Res2 Net; (**h**) Normalized Matrix of ResNet. Figure 13: Confusion Matrix of each model.