## 4 Experimental Results and Analysis
### Dataset 2: Beijing-Hyunchai Auto Enterprise  
In order to carry out change detection experiment on BEIJING-HYUNDAI AUTO Enterprise, a total of 38 images were selected from 9 July 2019 to 25 September 2020. The images were cropped and the experimental processing area was identified. As shown in Figure 14, the area marked by the red box is the experimental processing area, and the image connected by the red line is the optical image corresponding to its enlarged image. As shown in the enlarged image, the size of the experimental processing area is 128 \(\times\) 128 pixels.  
Figure 12: CFAR detection results and labeling results. (**a**) CFAR’s binary detection results of changed pixels (vehicles). (**b**) Detected changed pixel (vehicles) label on the original SAR image with red dot.  
Figure 13: Curve of vehicle number. (**a**) Logarithm Background Subtraction method result. (**b**) Pairwise comparison method result.  
After basic denoising of the images, the SAR-SIFT algorithm was used to perform registration on the experimental area. Matching points connection diagrams are shown in Figure 15. In order to visualize the matching effect, we use red lines to connect all detected matching pairs so that the registration results can be clearly observed.  
The registration effect is shown in Figure 16. After SAR-SIFT registration, the images were aligned, and further operations can be performed.  
Figure 14: Parking lot area for experiment. (**a**) SAR image acquired on 25 September 2020 by Sentinel-1A satellite. (**b**) Optical image of parking lot area from Google Earth. (**c**) SAR image of parking lot area acquired on 25 September 2020 by Sentinel-1A satellite.  
Figure 15: Line segments connecting matching points (date of reference image: 25 September 2020; date of image to be registered: 24 December 2019).  
The Logarithm Background Subtraction result of the image sequence is demonstrated in Figure 17. From left to right are the input image, the static background and the image that contains the changed part. The red box represents the parking area to be detected.  
Adding a binary mask to the image that contains the changed part leaves only the region of interest, which is convenient for subsequent CFAR detection and reduces the error caused by the irrelevant area. The region of interest is shown in Figure 18, where Figure 18a is an optical image, and the red box indicates the parking area of the enterprise, which is also the CFAR detection area. Figure 18b shows the binary mask.  
After obtaining the image that contains the changed part through Logarithm Background Subtraction, it is necessary to perform target detection in the region of interest of the image. The detection result is shown in Figure 19.  
The pairwise comparison method detection result is shown in Figure 20.  
The red pixels in the image represent the presence of vehicle targets compared to the background or base image, while green pixels represent the decrease of vehicle targets compared to the base image. By multiplying the number of detected target pixels by the proportional coefficient, the number of vehicles in each image can be calculated, resulting in a curve showing changes in vehicle count, as shown in Figure 21, of which Figure 21a shows the Logarithm Background Subtraction method result and Figure 21b shows the result of pairwise comparison method.  
Figure 16: Comparison image before and after SAR-SIFT registration. (**a**) Before SAR-SIFT registration. (**b**) After SAR-SIFT registration.  
Figure 17: Logarithm Background Subtraction result. (**a**) Input image. (**b**) Background. (**c**) Change part.