# Semantic Segmentation of Urban Remote Sensing Images Based on Deep Learning
## 4 Conclusions  
The ongoing advancements in remote sensing technology enable the acquisition of increasingly high-quality images. These images facilitate a precise understanding of urban expansion. This paper delves into the neural network-based image semantic segmentation method, focusing on the diverse plot information within remote sensing imagery as the subject of investigation.  
Initially, the labelme tool facilitated data annotation, segregating it into two distinct categories to assess the model's viability. The requisite torch operating environment was established, various network models underwent training, and analytical scrutiny of the experimental outcomes led to the selection of the most efficacious neural network model as the foundational network. To augment accuracy, the introduction of an attention mechanism was deemed necessary to enhance the evaluation metric. Consequently, spatial, SE, and channel attention mechanisms were integrated into the backbone network, and a comparative analysis was conducted to determine the optimal loss function. Through experimental comparison, it was found that CE_loss had a better performance than Focal_loss, because the classification imbalance in the dataset was not significant. Finally, CE_loss was selected as the loss function. After the analysis and comparison of the experimental results, spatial and SE attention mechanisms slightly improved the segmentation accuracy, but the channel attention mechanism had a more significant improvement, and the accuracy was increased by 1.14% compared with the basic network. The training time of the three groups was similar, but the training time was also about one-third longer than when the attention mechanism was not added. Finally, according to the experimental comparison, the channel attention mechanism was selected as the final model. The final experimental results show that the proposed Vgg+Unet+Channel network model had a good effect on small sample image segmentation.  
Conceptualization, J.L. and J.W.; methodology, J.L. and J.W.; software, J.W. and M.R.; validation, D.X. and M.R.; investigation, H.X. and M.R.; resources, J.L. and D.X.; data curation, J.L. and D.X.; writing--original draft preparation, J.L., J.W. and H.X.; writing--review and editing, J.W. and H.X.; funding acquisition, D.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 52074064; in part by the Natural Science Foundation of Science and Technology Department of Liaoning Province under Grant 2021-BS-054; in part by the Fundamental Research Funds for the Central Universities under Grant N2404013, N2404015.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Semantic Information** & **Number of Pixels** & **Percentage of Surface** \\ \hline Impervious surface & 13,704 & 0.20\% \\ Car & 21,243 & 0.32\% \\ Tree & 1,380,016 & 20.54\% \\ Low vegetation & 1,786,765 & 26.60\% \\ Building & 1,601,524 & 23.84\% \\ Background & 1,915,024 & 28.50\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The proportion of each semantic in Figure 15.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Semantic Information** & **Number of Pixels** & **Percentage of Surface** \\ \hline Impervious surface & 6927 & 0.14\% \\ Car & 37,879 & 0.79\% \\ Tree & 174,257 & 3.61\% \\ Low vegetation & 176,063 & 3.65\% \\ Building & 3,433,466 & 71.16\% \\ Background & 996,467 & 20.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: The proportion of each semantic in Figure 16.  
Data Availability StatementPublicly available datasets were analyzed in this study. This data can be found here: [[https://www.isprvs.org/education/benchmarks/UrbanSemLab/Default.aspx](https://www.isprvs.org/education/benchmarks/UrbanSemLab/Default.aspx)]([https://www.isprvs.org/education/benchmarks/UrbanSemLab/Default.aspx](https://www.isprvs.org/education/benchmarks/UrbanSemLab/Default.aspx)) (accessed on 9 February 2022).