## 3 Result and Discussion
### Comparison Experiment Results and Analysis  
To evaluate the performance of MFA-UperNet in mangrove segmentation, comparative experiments were conducted between the proposed MFA-UperNet and other models on the dataset constructed in this paper. The dataset consists of 1798 training images, 514 validation images, and 256 test images. The values shown in the table represent the average of five independent tests performed on the validation and test sets. Independent sample \(t\)-test analysis indicates that the performance differences between models are statistically significant (\(p<0.05\)). This paper uses mIoU, mPA, Params, and FLOPs as evaluation metrics. The segmentation results of different models for mangroves are shown in Table 3.  
As shown in Table 3, compared to methods such as DANet, DeepLab v3+, and Swin-UperNet, the proposed MFA-UperNet method achieves more precise recognition results, particularly excelling in mangrove boundary localization and detail preservation under complex backgrounds. It achieves optimal results across all evaluation metrics, including IoU, PA, mIoU, and mPA, primarily due to the innovative design of three core modules in the network: the feature encoder, Auxiliary Edge Neck (AEN), and the Cascade Pyramid Fusion Module (CPFM) along with Multi-scale Selective Kernel Attention Module (MSKAM) in the semantic decoder. The feature encoder employs the ConvNeXt architecture, whose powerful feature extraction capability enables the network to effectively capture multi-level information from local textures to global structures, providing a solid feature foundation for subsequent processing. The Auxiliary Edge Neck significantly improves segmentation accuracy in edge regions through lightweight fully convolutional network design and the introduction of hybrid loss functions, providing an effective solution to the problem of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**Mangrove**} & \multicolumn{2}{c}{**Background**} & \multirow{2}{*}{**mIoU**} & \multirow{2}{*}{**mPA**} & \multirow{2}{*}{**Params**} & \multirow{2}{*}{**FLOPs**} \\ \cline{3-3} \cline{6-9}  & & **IoU (\%)** & & & & & & \\ \hline DANet [24] & ResNet50 & 87.86 & 93.03 & 92.80 & 96.57 & 90.33 & 94.80 & 47.5 & 216.1 \\ PSANet [25] & ResNet50 & 89.56 & 94.43 & 93.79 & 96.83 & 91.67 & 95.63 & 56.8 & 205.1 \\ PSPNet [23] & ResNet50 & 90.56 & 95.03 & 94.39 & 97.12 & 92.47 & 96.07 & 46.6 & **182.6** \\ DeepLabv3+ [27] & ResNet50 & 89.61 & 94.44 & 93.82 & 96.86 & 91.72 & 95.65 & **41.2** & 183.3 \\ Swin-UperNet [32] & Swin Transformer tiny & 92.05 & 95.80 & 95.29 & 97.62 & 93.67 & 96.71 & 59.4 & 242.7 \\ K-Net [28] & Swin Transformer tiny & 88.31 & 93.36 & 93.07 & 96.67 & 90.69 & 95.02 & 250.1 & 441.3 \\ PIDNet [29] & PIDNet-S & 92.41 & 95.90 & 95.52 & 97.80 & 93.97 & 96.85 & 72.6 & 258.6 \\
**Ours** & ConvNeXt-tiny & **93.13** & **96.21** & **95.95** & **98.07** & **94.54** & **97.14** & 59.2 & 234.2 \\ \hline \hline \end{tabular} Note: Bold indicates best data.  
\end{table} 
Table 3: A performance comparison of different segmentation models on the MSSDBG dataset. blurred boundaries between mangroves and backgrounds. It not only provides additional gradient information for the backbone network but also offers new insights into edge enhancement theory in remote sensing image segmentation. The CPFM and MSKAM in the semantic decoder represent an innovative feature fusion and enhancement solution. CPFM integrates the Feature Pyramid Network (FPN) and Pyramid Pooling Module (PPM), employing a bidirectional feature fusion strategy from top-down and bottom-up, achieving an efficient fusion of multi-scale features. Meanwhile, MSKAM combines ECA channel attention and LSK Large Selective Kernel module to achieve precise channel selection and adaptive receptive field adjustment at different levels of the feature pyramid.