# MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
## 4 Results
### Comparison with State-of-the-Art Methods  
To evaluate the performance of the proposed model for road extraction from remote sensing images, we compared with several baseline and state-of-the-art methods: FCN [2], ResUNet [22], D-LinkNet [23], and SE-DeepLab [36]. ResUNet is built with residual learning and UNet; D-LinkNet is a variant of LinkNet architecture and added dilated convolution layers in the center part, which has achieved best performance in the CVPR DeepGlobe 2018 Road Extraction Challenge [27]; SE-Deeplab employs the structure of Deeplab v3 and incorporates a squeeze-and-excitation (SE) module. All these models are trained with the same learning rates and employ the same data processing to ensure fairness.  
Table 3 reports the quantitative experiment results of the compared methods on the DeepGlobe dataset. The accuracy of FCN is lower than those of other methods, which is mainly due to the loss of spatial details. The D-LinkNet with additional dilated convolution layers outperforms ResUNet, obtaining an improvement of 1.5% on the IoU score and 0.67% on the F1-score. The design of an SE module in SE-DeepLab improves the IoU score by 7.62% and the F1-score by 4.01% compared with the D-LinkNet.  
Compared with those methods, the proposed MSPNet achieves the best performance in OA, IoU, and F1-score. For example, MSPNet obtains an F1-score of 84.51% and an IoU score of 73.64%, which are better than SE-DeepLab by 1.78% and 2.27%, respectively. We attribute these significant performance gains to three factors. (1) Due to the symmetric network design with skip-connections, the proposed MSPNet is able to preserve low-level spatial details and thus extract roads with smoother boundaries from remote sensing images. (2) The strip pooling design in our proposed model enhances the embedding of linear features, which greatly improves the connectivity of roads; therefore, the proposed MSP module can further improve the segmentation accuracy. (3) The joint supervision of BCE and dice loss alleviates the class imbalance problem.  
Figure 7: Progression of loss values in the training process. \(K\) is set to 0.2 in this comparison experiment.  
The partially visualized segmentation results of our MSPNet and other methods are illustrated in Figure 8, which shows three examples from different scenes. The corresponding distributions of FP and FN are plotted in Figure 9. The results extracted by FCN are worse than those of other methods, which is mainly caused by the loss of spatial information after multiple downsampling operations in its early layers. The results of UNet and D-LinkNet are similar, and they also both contain many missing connections and FP pixels. The SE-Deeplab shows better road connectivity compared with other methods, and it obtains the second-best results. In comparison, our proposed method extracts roads with better connectivity and smoother road edges. The roads segmented by other methods may be interrupted especially in some regions where occlusions exist, while our MSPNet recovers the connectivity very well by effectively capturing long-range dependencies along road regions. For example, in the results of rural areas (the first row in Figure 8), there are several residential houses in the upper-right and lower-left corner of the image, which are planted many trees on both sides; the results extracted by other methods contain some broken road segments and fail to maintain the connectivity, but the result extracted by MSPNet is consistent with the ground truth. In addition, in the results of densely connected road areas (the second and third row in Figure 8), other methods recognize some branch roads as background, while the proposed methods are consistent with those of ground truth, and very few FP pixels exist. These visualized segmentation results verify the superiority of our MSPNet in the task of road extraction from remote sensing images.