## 3 Result and Discussion
### Ablation Experiment Analysis
Detail textures preserved in lower-level features improved localization accuracy, while semantic information from higher-level features enhanced category recognition capability, thereby significantly improving the overall segmentation performance of mangrove wetlands. After adding CLloss, mIoU increased by 0.34% and mPA by 0.19%, demonstrating that the introduction of CLloss hybrid loss function effectively addressed the sample distribution imbalance issue. By combining cross-entropy loss and Lovasz-Softmax loss, it maintained overall classification accuracy while optimizing boundary region segmentation precision, showing particular advantages in handling cases with significant quantity disparities between mangrove and background categories. Following the addition of AEN, mIoU increased by 0.18% and mPA by 0.04%, showing that the introduction of the AEN auxiliary edge segmentation branch enhanced the model's ability to recognize mangrove wetland boundary regions. Through specialized edge learning and auxiliary supervision of the third-layer features, it effectively improved the blurring issues at boundaries between mangroves and surrounding vegetation, enhancing segmentation refinement. After incorporating MSKAM, mIoU increased by 1.47% and mPA by 1.03%, indicating that MSKAM significantly enhanced the model's ability to recognize mangrove wetlands in complex backgrounds. Through the adaptive enhancement of shallow feature channels by the ECA module and the dynamic adjustment of deep feature receptive fields by the LSK module, it achieved more precise feature extraction and spatial information capture, effectively addressing issues such as scale variations and shape differences of mangrove wetland targets in remote sensing images. Therefore, through these ablation experiments, this paper validated the effectiveness of the proposed modules. Meanwhile, to more intuitively demonstrate the effectiveness of the modules proposed in this paper, we visualized the results of both individual modules and the combination of multiple modules. Through this approach, we can clearly showcase each module's contribution to the model's performance improvement, as shown in Figure 13.  
In the baseline method, the model showed notable limitations in mangrove identification, exhibiting misclassification phenomena and obvious blurring effects in boundary regions. After introducing CPFM, due to its effective fusion of multi-scale feature information, the model's overall segmentation performance improved significantly, with notably reduced misclassification areas, although some inaccuracies in boundary processing remained. Following the addition of CLloss, through the optimization of mixed loss functions, the model demonstrated better performance in handling sample imbalance issues and improved boundary region segmentation accuracy, though the capture of detailed features in complex scenarios still needed improvement. With the further introduction of the AEN module, the model's ability to recognize edge regions was enhanced with clearer boundary contours, but some difficulties remained in processing small-target mangroves. Finally, after incorporating MSKAM, the model's performance reached its optimal state. Through the synergistic effect of ECA and LSK modules, feature extraction became more precise and \begin{table}
\begin{tabular}{c c c} \hline
**Method** & **mIoU (\%)** & **mPA (\%)** \\ \hline Baseline & 88.31 & 93.36 \\ Baseline + CPFM & 92.55 & 95.88 \\ Baseline + CPFM + CLloss & 92.89 & 96.07 \\ Baseline + CPFM + CLloss + AEN & 93.07 & 96.11 \\ Baseline + CPFM + CLloss + AEN + MSKAM & **94.54** & **97.14** \\ \hline \end{tabular} Note: Bold indicates best data.  
\end{table} 
Table 5: Ablation study results of the model under different settings. spatial information acquisition more comprehensive, enabling the model to better handle targets of different scales, particularly achieving significant improvements in small target recognition and boundary detail processing in complex backgrounds.