## 3 Results
### Experimental Results on Jilin-1 Satellite Video
Therefore, the area of the vehicles is expected to be less than  
Figure 5: The standard road map (**a**) and road map produced by the convolutional neural network (**b**) from the Jilin-1 video. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **TP** & **FP** & **FN** & **P** & **R** & **F** \\ \hline Value & 172,585 & 34,377 & 7537 & 0.83 & 0.96 & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy evaluation of registration between source and reference image in the Jilin-1 video. 192 pixels. The length-to-width ratio should be less than 6. Based on these rules, moving vehicles in the video were finally obtained. The results of GMM, IFD, ViBe, and our method are shown in Figure 6, and the quantitative evaluations are shown in Table 4. From Table 4, we can see that the R values of IFD and ViBe are very high, but their P values are very low, which restricts the performance of these methods. This demonstrates that these methods can effectively identify moving objects, but they misidentified many false moving objects. They failed to distinguish false and pseudo moving objects caused by inaccurate registration and/or parallax from real ones. Compared to these methods, the performance of our method is much better. It achieves  
the same way as in the Jilin-1 experiment, we illustrated the matching results via several connections. The selected key points and their matched ones are shown in Figure 7. We adopted the same strategy to Jilin-1 to evaluate the registration accuracy. The registration accuracy is shown in Table 5.  
We conducted several experiments following the same model selection scheme to determine the best parameters. The parameters of the background model were set as follows for the Skysat video: \(N=20\), \(R=8\), \(T=2\), and the random sampling probability was \(1/4\). The roads were extracted using the improved neural network. The generated road map is shown in Figure 8, and the quantitative assessment results are in Table 6. We can see that the road extraction result from the Skysat video is a little worse than that from Jilin-1. The reason is that Jilin-1 provides a color image with three bands, so that the shape and texture features are more pronounced, while Skysat is a single-band instrument, and hence contains much less information. Finally, morphology analysis was used to filter false objects further. A similar strategy was applied for morphology parameters, considering the spatial resolution of the Skysat satellite. Namely, the object length ranges from 2 to 20 pixels, and the width ranges from 1 to 6 pixels. Therefore, the area of the vehicles is typically less than 120 pixels. The length-to-width ratio is typically less than 6. The results of the GMM, IDF, ViBe, and our method are shown in Figure 9 and the quantitative evaluations are shown in Table 7. All the methods were applied to the aligned video. From Figure 9, we can clearly see the superiority of our method. Other methods misidentify the tower as a moving object. Apparently, the motion of the tower is caused by parallax, which is rendered as a moving object in the video. Other methods have difficulty in eliminating this kind of pseudo moving object. In contrast, our method can remove them by leveraging the road information additionally extracted. The quantitative analysis shows similar results to the Jilin experiment. The performance of the proposed method is much better than those of the contrast methods. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **TP** & **FP** & **FN** & **P** & **R** & **F** \\ \hline Value & 106,944 & 117,835 & 1838 & 0.48 & 0.98 & 0.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy evaluation of registration between source and reference image in Skysat video. Figure 9: The moving vehicles of the 412 th frame of Skysat video detected by GMM (**a**), IFD (**b**), ViBe (**c**) and our method (**d**).