## 3 Results
### Experimental Results on Jilin-1 Satellite Video  
Image registration, as the main pre-processing step, was first performed to ensure that all frames were aligned in the same spatial coordinate system. The 258 th frame was randomly selected as an example. A total of 721 key points were selected from the reference imagery (i.e., the corrected 257 th frame), and 729 key points were selected from the 258 th frame. A total of 529 key points were successfully matched. The image would become heavily cluttered if all the matched key points were shown. Therefore, only a few groups of connections are shown in the illustration. The sketch is shown in Figure 4, where the colored dots represent the detected key points. We sampled some control points at fixed intervals in the reference imagery. Then, they were used to evaluate the registration accuracy according to the above-mentioned method. The registration results are listed in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & **Number of** & **Mean Value of** & **Mean Value of** & \\
**Reference** & **Registration** & **x Axis** & **y Axis** & **Valid Points** \\
**Band** & **Band** & **Direction** & **Direction** & \\ \hline \hline \(1\) & \(1\) & \(0.16\) & \(0.19\) & \(79\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy evaluation of registration between source and reference image in Jilin-1 video. Figure 4: Sketch of matched key points between reference (revised the 257 th frame) and the 258 th frame. The color points refer to the key points detected in each imagery of Jilin-1. Figure 3: The labeled real moving vehicles of Jilin-1 (**a**) and Skysat (**b**). Moving objects were then detected. We conducted many experiments following the rule of the single variable to determine the best parameter. The parameters of the background model were set as follows, empirically via model selection, for the Jilin-1 video. The background samples of each pixel were set to 20, namely, N = 20. The radius R was set to 10. If the difference between the current frame pixel and each sample exceeds this radius, it will be labeled as 1; otherwise, it will be labeled as 0. The threshold T was set to 2; if the number of pixels labeled as 1 exceeds T, it will be regarded as foreground; otherwise, it will be regarded as background. The random sampling probability was set to 1/4, which means that if a pixel is regarded as background, it has a probability of 1/4 of updating its samples. Concurrently, it has a probability of 1/4 of updating the samples of the neighborhood pixels. The above process can detect most moving objects, but the pseudo moving objects cannot be identified. The false and pseudo moving objects are mainly located at the edges of the buildings, bridges or storage tanks. Road recognition was conducted using the improved convolutional neural network. Roads are the main activity area for moving objects in the video frames. In order to assess the performance of the model quantitatively, we labeled the standard road map manually. The standard and produced road maps are shown in Figure 5. The quantitative assessment of road extraction is shown in Table 3. From the results, we can see that the improved neural network can achieve high accuracy on Jilin-1 satellite imagery. The produced road map will be used as a regional filter to improve the detection accuracy of moving vehicles. Many false and pseudo moving objects distant from roads have been removed. The morphological filter was then used to further remove the remaining false objects on the road. Given the known spatial resolution, we can infer the sizes of vehicles in the imagery. However, in consideration of the mixed pixels, we doubled the size of our estimation to be conservative. The length of vehicles is usually between 3 and 16 m, so in the imagery it corresponds to approximately 3 to 32 pixels. The width ranges from approximately 1.5 to 3 m, corresponding to one to six pixels.