# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
## 5 Discussion  
Our experiments on three datasets showed that MFFNet delivers impressive results in the extraction of buildings. There were slight variations in the segmentation results of MFFNet across these three datasets, which could be attributed to the differences in building materials and scales in different regions. However, when compared to other deep convolutional networks on the same dataset, MFFNet consistently achieved the best segmentation results. This indicates that MFFNet can be effectively used to extract buildings from high-resolution remote sensing images of various sources. When working on the Jilin-1 dataset, since this dataset was manually annotated based on satellite images, some buildings in the images were difficult to identify, leading to various degrees of mislabeled results or omitted labels becoming mixed into the training, validation, and test sets. Among the five comparison models, MFFNet achieved the highest evaluation metrics (as shown in Figure 11), indicating that MFFNet possesses superior noise resistance. Even when the training, validation, and test sets contain certain noise levels, MFFNet still delivers superior segmentation results on complex buildings. As illustrated in group 3 of Figure 8, we highlighted a rectangular area in the original image where the top of the building was slightly covered by vegetation. MFFNet segmented the target more accurately than other models. Satellite data cannot provide more apparent detailed expressions, and other comparison networks tend to mistakenly segment the middle road as part of the building, whereas MFFNet largely avoids this issue. Additionally, the shooting angle of satellite remote sensing images is not vertical, resulting in larger shadows of  
Figure 10: Comparison of experimental results on the WHU dataset. Each row represents a set of images segmented by different models. The red rectangle highlights areas of particular interest. buildings. MFFNet can accurately segment buildings under shadows. Due to various factors affecting satellite remote sensing images, their imaging quality is inferior compared to drone data, leading to slight noise in the data. The experiments show that MFFNet can mitigate the impact of noise on segmentation results, making MFFNet highly effective for extracting buildings from high-resolution satellite remote sensing images. As shown in Figure 12, MFFNet achieved the best evaluation metrics in the multi-model comparison applied to the Massachusetts dataset. The UNet series networks also commendably performed, with their evaluation metrics consistently surpassing those of networks characterized by spatial pyramid structures. As can be observed from Figure 9, networks with spatial pyramid structures tend to produce \"sticky\" results when segmenting the Massachusetts building dataset, where buildings are often merged. This phenomenon might be attributed to the high density and quantity of buildings within the spatial scope of the Massachusetts dataset. The spatial pyramid structure struggles to capture the information of these small buildings, whereas the UNet series networks excel at integrating semantic information from different scales, effectively extracting the buildings. This capability is one of the reasons why the UNet series networks have garnered widespread attention among researchers. MFFNet, possessing both the characteristics of the spatial pyramid structure and the UNet series networks, achieves higher accuracy than the UNet series networks, making it highly effective for extracting buildings from high-resolution remote sensing images. The WHU building dataset comprises high-resolution satellite remote sensing images and drone high-resolution remote sensing images. Moreover, it is a large-scale remote sensing dataset. Supported by big data, the segmentation performance of each network is commendable (as shown in Figure 13). However, MFFNet has a more distinct segmentation advantage. On the one hand, MFFNet can fuse multiple features, delineating more accurate building boundaries.