## 3 Result and Discussion
### Comparison Experiment Results and Analysis
This design not only effectively addresses the challenges of complex backgrounds and variable scales in mangrove remote sensing images while maintaining computational efficiency but also provides an innovative solution to the problem of multi-scale feature representation in remote sensing image analysis. To more intuitively understand the differences between the proposed method and other methods, visualization results are presented in Figure 10, where red regions represent identified mangroves, blue regions represent non-mangrove areas, and in the second column, black and gray regions correspond to background and Ground Truth, respectively. Through comparative analysis, the method in this chapter demonstrates clear advantages in several key aspects. The generated segmentation boundaries more closely align with the actual mangrove distribution range. (Figure 10a,b) In handling complex background areas, this chapter's method shows stronger robustness, effectively avoiding the common category confusion problems seen in other models, which is particularly evident in the results shown in the third row. (Figure 10c) Notably, in the completeness test shown in the fourth row, only this chapter's method successfully achieved complete coverage of the target area, while other models showed varying degrees of omission. (Figure 10d) Considering all metrics comprehensively, the method in this chapter demonstrates significant advantages in both segmentation accuracy and boundary precision. However, through an in-depth analysis of the segmentation results, we also found that MFA-UperNet still has room for improvement in certain challenging scenarios. In areas with dense mangrove distribution, due to spectral confusion caused by vegetation canopy overlap in remote sensing images, the model's delineation of mangrove boundaries is not  
Figure 10: A plot of the segmentation results of different models on the MSSDBG dataset. (White boxes are used to highlight the differences in the results under different methods.) precise enough. In areas with complex lighting conditions, especially in remote sensing images with strong shadows or uneven illumination, the segmentation performance shows slight degradation. In transition zones between mangroves and other coastal vegetation, due to the similarity of the spectral features in remote sensing images, there exists some uncertainty in the model's discrimination between mangrove and non-mangrove areas. Furthermore, in coastal areas affected by tides, spectral variations caused by water coverage also pose challenges to accurate mangrove identification. From a computational efficiency perspective, the Parameters and FLOPs of our proposed model have increased compared to lightweight networks such as DANet and PSANet. This suggests that one important direction for future research is to further reduce the model's parameter count and computational complexity while maintaining or improving current segmentation accuracy through optimizing network structure design, exploring more efficient feature extraction and fusion strategies, and adopting model compression techniques, thereby achieving a better balance between efficiency and performance. This has significant practical implications for model deployment in real-world applications. As shown in Figure 11, the curves illustrating changes in Loss values and accuracy are presented. These graphs comprehensively reflect the training process and performance of the model.