## 4 Experimental Results and Analysis
### Dataset 1: Nearby Parking Lot  
For the detection of the change of the number of vehicles in the nearby parking lot, a total of 82 images were selected from 5 March 2020, to 14 November 2022. First, the experimental processing area was selected with an image size of 376 \(\times\) 376 pixels, as shown in Figure 7.  
After applying median filtering and radiometric correction to the image sequence, image registration is required. There are two main methods for image registration: pixel-based registration and feature-based registration. Pixel-based registration methods minimize the differences between pixels in the images to achieve image registration. Commonly used pixel-based registration methods include cross-correlation registration, phase correlation-based registration, and wavelet transform-based registration. The principle behind these methods is to calculate the similarity between corresponding pixels in two images, find the translation that maximizes the similarity, and complete image registration. Feature-based registration methods achieve image registration by extracting feature points or regions from the images and calculating the relative positions of these feature points or regions. In this paper, SAR-SIFT registration and cross-correlation registration were applied to the image sequence to demonstrate the registration results using a scene as an example. The SAR-SIFT algorithm was used to register the images. Matching points connection diagrams are shown in Figure 8. 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. Figure 9a shows the intensity superposition of the reference image and the image to be registered before registration, where gray represents areas of equal intensity, and green and magenta represent areas of different intensities. Figure 9b shows the superposition after SAR-SIFT registration, and Figure 9c shows the superposition after cross-correlation registration. Figure 7: Comparison of optical image and SAR image of the experimental area. **(a)** Optical image from Google Earth. **(b)** SAR image acquired at 15 Septmber 2022 by Sentinel-1A satellite. By comparing the intensity overlay images of the two registration methods, it can be seen that the SAR-SIFT algorithm can accurately achieve the spatial alignment of two images, while the effect of cross-correlation registration is not ideal. Image registration is one of the key steps in this paper and is crucial for ensuring data quality and detection accuracy. Low registration accuracy will affect subsequent target detection ability and ultimately increase the experimental result errors. Therefore, SAR-SIFT algorithm with high accuracy is used to register the temporal images in this experiment for subsequent processing. After registration, images need to be cropped for background subtraction. The main reason for cropping is that the areas that changed before and after are too small in the experimental images. The above operation can improve the proportion of changed regions in the entire image, and the final cropped size is 50 \(\times\) 50 pixels. As seen above, Figure 10a is the area of registration, while Figure 10b and Figure 10c are the optical and SAR image of the parking lot, respectively. Logarithm Background Subtraction is applied to the area marked by the red box in Figure 10, and the resulting image is shown in Figure 11. 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. Figure 8: Line segments connecting matching points (date of reference image: 15 Septmber 2022; date of image to be registered: 21 October 2022). Figure 9: Comparison image before and after SAR-SIFT registration. (**a**) Before SAR-SIFT registration. (**b**) After SAR-SIFT registration. (**c**) After cross-correlation registration.