## 3 Experiment
### Comparison with State-of-the-Art Methods  
In this module, we compare the GLFFNet proposed in this paper with some excellent similar models. The experiment results of all models on the Potsdam dataset are shown in Table 5. The experiment results show that the GLFFNet proposed achieves an excellent result of 87.96% on MIoU, surpassing other models. It should be additionally noted that our research is mainly aimed at improving the accuracy of the model and therefore has not yet reached a very satisfactory state in terms of inference time. However, compared with most of the other Transformer-like large models, our inference time still has some advantages since our auxiliary training module is not involved in inference. Of course, we will continue to improve the model to further enhance its overall performance. The experiment results of all models on the GID dataset are shown in Table 6. The experiment results show that the GLFFNet proposed achieves an excellent result of 80.42% on MIoU. This result further demonstrates the usefulness of GLFFNet for the problem of object diversity in the image and how GLFFNet can be applied to a general environment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MIoU (\%)** & **MAcc (\%)** & **PAcc (\%)** \\ \hline single-head loss & 86.74 & 92.57 & 93.64 \\ multi-head loss & **87.96** & **93.84** & **94.75** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of results of Multi-head Loss Block ablation experiment. Figure 6: The training result of multi-head loss. Figure 7 shows the partial segmentation result on the Potsdam dataset. Due to limited space, only the four segmentation results of GLFFNet (ours) and UNetFormer that are the best effects in the selected comparison model are shown. Figure 7 consists of four columns. The first column is the original image, the second column is the labeled image, the third column is the segmentation result of GLFFNet, and the fourth column is the segmentation result of UNetFormer. To make a better comparison, we have circled the key positions with red ovals. In the first set of images, the two cars on the road in the upper right are very closely spaced, which makes accurate recognition by the model very difficult. The presence of the interval between the two cars is clearly visible in the recognition results of our GLFFNet (third column), while in the recognition results of UNetFormer (fourth column), the road between these two cars is mis-segmented. In the second set of images, the object in the top right corner is too small to be recognized with any difficulty by the human eye in time. But the features of the pixels occupied by these small objects, which are different from their surroundings, are still captured by our GLFFNet. In the third set of images, a white car at the bottom edge is slightly obscured by these tree branches next to it, and even I am confused as to whether to mark it all as a car here or show the presence of the branch. Of course, according to the officially labeled data, there are tree branches here. So in our recognition results, there are these branches present, but of course, this is too small, and the outline is still not perfect. From the recognition results of UNetFormer, the car is accurately identified, completely ignoring the tree branches on it. In the actual scenario, there should be different specific uses for ignoring the tree branches or not. In the next two sets of images, the UNetFormer still has the problem of ignoring small objects, but, in this case, our model still does some justice to these small objects, although the segmented edges are not complete enough. In conclusion, it can be seen from the red marks in the image that GLFFNet has better segmentation ability for small and weak objects in complex scenes in remote sensing images.