# Remote Sensing Road Extraction by Road Segmentation Network
## 3 Experiments  
In the experiments, both the DeepGlobe dataset [17] and Massachusetts dataset [18] are used and five compared methods were considered: the FCN [19], the UNet [20], the CasNet [21], the ResUNet [22], and the D-LinkNet [23]. Among them, FCN has the sequential operations of down-sampling in early layers. CasNet uses the VGG-Net as an encoder network, while ResUNet, D-LinkNet, and the proposed method use the ResNet34  
Figure 3: Designed refined network (DRN). as an encoder network. All the compared methods use cross-entropy as the training loss and employ the same data processing for fairness. Table 1 shows the quantitative results on the DeepGlobe dataset. Numerically, the proposed method achieved the best performance on three metrics of OA, P, and F1. D-LinkNet uses dilated convolution to increase the receptive fields on the feature, and it obtained the highest performance on recall. ResUNet had an advantage in P, and was only 0.23% lower than our proposed method. The results of FCN and UNet were worse than that of other methods, which was mainly caused by the loss of road context information after multiple down-sampling operations on different features. Table 2 reports the comparative results on the Massachusetts dataset. Compared with D-LinkNet, the proposed method increased 0.14%, 1.24%, and 0.41% on OA, P, and F1, respectively. The original encoder structure of UNet leads to poor performance in the four metrics. Regarding the improvement of the encoder structure, the ResUNet combines residual learning in its encoder, the CasNet uses VGG-Net, and D-LinkNet uses ResNet as its encoder. They had advantages of 2.16%, 1.15%, and 3.28% compared to UNet on P, respectively. To observe the performance of different designs in the proposed method, Table 3 shows the results of the ablation experiment on the Massachusetts dataset. The Base method only uses ResNet as an encoder and multiple deconvolution layers as a decoder. AM is the attention mechanism to enhance the context feature from the inter-layer, and DRN is the network to refine road maps. The last line represents the proposed method, and the reciprocal of road frequency was used as weight in the loss function. Compared to the base method, the AM contributed 3.26% performance improvement on P, and the DRN showed an improvement of 0.6% on P compared to the Base combined with AM method. Our proposed method further promoted 0.21%, while the R score dropped by 0.01%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline FCN & 97.42 & 77.98 & 51.16 & 61.78 \\ UNet & 97.68 & 74.68 & 60.44 & 66.81 \\ CasNet & 97.66 & 75.72 & 63.41 & 69.02 \\ ResUNet & 98.02 & 79.76 & 62.76 & 70.24 \\ D-LinkNet & 98.10 & 76.57 & **71.46** & 73.93 \\ Our & **98.25** & **79.99** & 70.00 & **74.66** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the comparative experiments on the DeepGlobe dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline FCN & 97.35 & 74.83 & 53.36 & 62.29 \\ UNet & 97.55 & 75.06 & 55.47 & 63.79 \\ CasNet & 97.49 & 77.22 & 51.60 & 61.86 \\ ResUNet & 97.43 & 76.21 & 54.62 & 63.63 \\ D-LinkNet & 97.54 & 78.34 & **56.69** & 65.78 \\ Our & **97.68** & **79.58** & 56.66 & **66.19** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the comparative experiments on the Massachusetts dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline Base & 97.43 & 75.51 & 54.64 & 63.40 \\ Base + AM & 97.57 & 78.77 & 55.52 & 65.13 \\ Base + AM + DRN & 97.62 & 79.37 & **56.67** & 66.12 \\ Our & **97.68** & **79.58** & 56.66 & **66.19** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the ablation study on the Massachusetts dataset.