## 3 Result and Discussion
### Ablation Experiment Analysis  
In this section, we conduct ablation experiments and performance analysis on the key modules in MFANet to validate their effectiveness. First, we discuss the performance differences between the ConvNeXt network and the Swin Transformer as encoders for the MFANet. Then, we evaluate the effectiveness of the CPF module, MECA module, and AEN module in MFANet while also assessing the impact of different loss functions on the mangrove segmentation results. The encoder used in the MFANet is the ConvNeXt network. Figure 12 presents a comparison of the improvement effects between the ConvNeXt network and the Swin Transformer. Figure 12 illustrates the improvement effects of the five components in the ConvNeXt network and provides a direct comparison with the Swin Transformer. Compared to Swin-T, ConvNeXt-T achieves a 0.7% improvement in accuracy on the ImageNet dataset under the same GFIOPs. Moreover, it outperforms the Swin Transformer on the COCO detection dataset and the ADE20K segmentation dataset. Therefore, we employ ConvNeXt-T for the semantic segmentation task of mangrove remote sensing images. Figure 11: Plot of Loss and Accuracy. To validate the effectiveness of the backbone network in the MFA-UperNet model, experiments were performed using ConvNeXt-small, ConvNeXt-tiny, and ConvNeXt-base as the backbone networks. This was performed to compare the impact of different backbone network levels on the segmentation results, which aimed to achieve a better balance between segmentation accuracy and inference speed. Table 4 presents the experimental results and the number of parameters for each level of the backbone networks. The experimental results show that ConvNeXt-T achieved relatively optimal segmentation results on the MSSDBG dataset, and it was worth noting that ConvNeXt-T achieves the lowest parameter count of 59.24 M, which was 21.64 M and 61.82 M less than ConvNeXt-S and ConvNeXt-B, respectively. Although ConvNeXt-T's FLOPs was 234 G, which was lower than ConvNeXt-S and ConvNeXt-B, the combination of its lowest number of parameters and highest accuracy proved that ConvNeXt-T better processed the semantic features of the mangrove forests in the MSSDBG, so ConvNeXt-tiny was finally chosen as the encoder backbone network for the model. To comprehensively evaluate the network architecture proposed in this paper, we conducted systematic ablation experiments on key modules using our constructed dataset and performed an in-depth analysis of the results. This section focuses on evaluating the effectiveness of several core modules proposed in this paper, including CPFM, CLloss, AEN, and MSKAM modules. Using FPN as the baseline, we verified their effectiveness through a progressive addition of each module. The results are shown in Table 5. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**Mangrove**} & \multicolumn{2}{c}{**Background**} & \multicolumn{1}{c}{**mIoU**} & \multicolumn{1}{c}{**mPA**} & \multicolumn{1}{c}{**Params**} & \multicolumn{1}{c}{**Flops**} \\ \cline{2-9}  & **IoU (\%)** & **PA (\%)** & **IoU (\%)** & **PA (\%)** & **(\%)** & **(\%)** & **(M)** & **(G)** \\ \hline ConvNeXt-T & **93.03** & **96.14** & 95.52 & 98.02 & 94.27 & **97.08** & **59.24** & **234** \\ ConvNeXt-S & 92.90 & 96.05 & **95.82** & **98.03** & **94.36** & 97.04 & 80.88 & 256 \\ ConvNeXt-B & 92.59 & 95.95 & 95.63 & 97.88 & 94.11 & 96.92 & 121.02 & 292 \\ \hline \hline \end{tabular} Note: Bold indicates best data.  
\end{table} 
Table 4: The experimental results of different backbone networks on the MRSDBG dataset. Figure 12: ConvNeXt Improvement and Effectiveness Comparison Chart. As shown in the results from the above table, after incorporating CPFM, mIoU increased by 4.24% and mPA by 2.52%, indicating that CPFM effectively integrated multi-scale feature information of mangrove wetlands through the cascaded fusion of Feature Pyramid Network and Pyramid Pooling Module.