# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
## 6 Conclusions  
In this paper, we introduce a novel multi-feature fusion network (MFFNet) that significantly enhances the accuracy of building extraction from high-resolution remote sensing images using deep convolutional networks and improves the detail of the extraction results. The network utilizes an encoder-decoder architecture, merging modules for deep semantic information extraction with those for multi-scale spatial semantic extraction. It employs various attention mechanisms and the atrous spatial pyramid pooling (ASPP) module to effectively capture diverse feature information, enabling precise building extraction. The experimental results on the Jilin-1 building dataset, Massachusetts building dataset, and WHU building dataset indicate that MFFNet outperforms other traditional models in critical evaluation metrics such as mean intersection over union (MIoU), accuracy, and F1-score. MFFNet's successful deployment across multiple datasets highlights its exceptional performance and utility in extracting buildings from high-resolution remote sensing images from diverse sources. Notably, in datasets with small samples, MFFNet demonstrates remarkable noise resistance and accuracy, underscoring its substantial potential for real-world applications. Despite MFFNet's significant advancements in processing high-resolution remote sensing data, it still encounters challenges in handling particularly complex terrains and urban landscapes, signifying a direction for future enhancement.  
Conceptualization, K.L. and Y.X.; methodology, K.L. and Y.X.; software, K.L.; validation, K.L., J.L., W.Z. and Y.Z.; formal analysis, K.L. and J.L.; investigation, K.L., W.Z. and Y.Z.; resources, J.L., W.Z. and Y.Z.; data curation, K.L., W.Z. and Y.Z.; writing--original draft preparation, K.L.; writing--review and editing, W.Z. and Y.Z.; visualization, K.L.; supervision, Y.X.; project administration, K.L.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
The research was undertaken thanks to funding from the Priority Academic Program Development of Jiangsu Higher Education Institution.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **Model** & **MIoU** & **Accuracy** & **Precise** & **Recall** & **F1-Score** & **Kappa** \\ \hline \multirow{3}{*}{Jilin-1} & MFFNet & 89.69\% & 97.05\% & 94.66\% & 94.25\% & 94.82\% & 92.63\% \\  & MFFNet (without ASPP) & 88.70\% & 96.66\% & 94.14\% & 93.63\% & 94.22\% & 91.90\% \\  & ViT & 89.27\% & 95.60\% & 94.42\% & 94.10 & 94.12\% & 92.62\% \\ \hline \multirow{3}{*}{Massachusetts} & MFFNet & 81.76\% & 94.00\% & 89.02\% & 90.11\% & 89.50\% & 87.43\% \\  & MFFNet (without ASPP) & 81.07\% & 93.69\% & 88.61\% & 89.61\% & 89.04\% & 86.86\% \\  & ViT & 79.15\% & 93.07\% & 86.78\% & 88.91\% & 87.76\% & 85.35\% \\ \hline \multirow{3}{*}{WHU} & MFFNet & 91.82\% & 98.73\% & 95.69\% & 95.50\% & 96.22\% & 94.22\% \\  & MFFNet (without ASPP) & 91.94\% & 98.78\% & 95.81\% & 95.55\% & 96.42\% & 94.28\% \\ \cline{1-1}  & ViT & 92.10\% & 98.50\% & 95.71\% & 95.83\% & 95.72\% & 95.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Segmentation results of MFFNet, MFFNet (without ASPP), and ViT.