As the primary data for topographic surveying and mapping, ground control points provide important control information for the production of military and civilian basic surveying and mapping products. Traditional ground control point collection mainly relies on a global satellite navigation system and field collection, which is time-consuming and labor-intensive. With the significant improvement of domestic synthetic aperture radar (SAR) satellite orbiting technology, it is now possible to automatically extract a wide range of ground control points using domestic spaceborne SAR images. As an essential feature target in the road environment, lamp posts have the characteristics of a wide distribution range and stable target. As the typical robust targets in the natural environment, lamp posts have become one of the best choices for ground control point alternatives. Due to the particular imaging method of SAR image, the lamp post target feature presents as an isolated point target scatterer in medium-low resolution spaceborne SAR images. However, there are many point target scatterers in the natural scene, and the influence of image speckle noise makes it more difficult to extract the lamp post target directly in SAR images. Therefore, the effective extraction of the lamp post target in SAR images has become the primary issue in studying ground control point extraction using SAR images.

The main flow of the method is as follows: 1) The optical image is sharpened using the image morphology closed-computing method to improve the expression of narrow and dark target information in the image. Combining with the sun’s altitude angle at the moment of acquiring the optical image, Gabor filtering is performed on the image to enhance the narrow and dark information of the image. 2) Any lamp post target in the narrow and dark information-enhanced image is selected as a template. Then, the template is searched for using NCC matching in the filtered image to obtain the lamp post target points. The template is searched to get the lamp post target point set. DBSCAN clustering parameters are set to realize the clustering of the lamp post target point set and obtain the rough geographic coordinates of the lamp post target. 3) The range Doppler (RD) model is used to iteratively solve the problem. The rough coordinates of the lamp post target are inversely encoded to the SAR planar coordinate system. The rough image point coordinates of the lamp post target in the SAR image are obtained. 4) According to the point prediction of the rough image point coordinates of the lamp post obtained, the lamp post target is located in the search window. According to the initial search results of the lamp post target, the strongest point target of backward scattering within the window is searched for. The RANSAC algorithm is used to extract the image point coordinates of the high-precision search result points to correct the direction of the search target points. 5) The lamp post target is accurately searched for. The image around the search point is upsampled. The image is interpolated using the bilinear interpolation method. The center of gravity method is used to find the point of the maximum value of the intensity of backward scattering, which is the sub-image where the lamp post target is located.

To verify the effectiveness of the method, a road in the north of Zhengzhou City, Henan Province, is selected as an example experimental area for this experiment to analyze the extraction steps. After processing with the narrow and dark target extraction method, the bright targets such as road markings are removed from the original image. The shadow information of the lamp posts is better expressed (Fig. 3). Gabor filtering is applied to the image after image sharpening. The shadow targets of the lamp posts in the Gabor filtered image (Fig. 4) are swollen and highlighted, and the shadow information is enhanced. Any enhanced shadow of the lamp post is selected as the matching template. The cluster classes obtained from NCC template matching can express the geographic location of the lamp post target after clustering by DBSCAN algorithm (Fig. 6). After the geographic coordinates of the lamp post target extracted from the high-resolution optical image are backcoded into the radar coordinate system, the backcoded lamp post target cannot be accurately matched with the lamp post target in the SAR image due to errors (Fig. 7). Taking the coordinates of the rough image point of the lamp post as the center, the experimental initial search results show that due to the complexity of the features on both sides of the road, some of the point search results have obvious search errors (Fig. 8). According to the constraint correction direction obtained by the RANSAC method and limiting the third quadrant constraint criterion, we re-establish the window to search for strong backscattering points (Fig. 9). The sub-image element location of the lamp post target is further obtained through the point target analysis method, and the lamp post target location in the SAR image is obtained (Fig. 11). After accuracy verification, it is found that the detection error of this method is 1.45 image elements from the visual interpretation of the SAR image. Considering the influence of the resolution of the SAR image and the interpretation error, this method has a high detection accuracy. In addition, to verify the generalization of this method, the experimental area b is increased for generalization experiment discussion. The results show that this method has achieved better results in extracting different types of lamp posts, and the detection error is 0.75 image elements, which verifies that the proposed method has strong generalization.

In our study, a lamp post extraction method for SAR images using optical shadow features for point location prediction is designed. The extraction of lamp post targets in high-resolution optical images using shadow features is realized through narrow and dark information enhancement and template matching. On the premise of obtaining the rough geographic location of the lamp posts, the point location prediction of lamp post targets in SAR images is realized by the RD model, and the detection of lamp post targets in SAR images is realized by combining with the constraint-corrected point target search strategy. High-resolution UAV images covering a road in the Zhengzhou area and GF-3 SAR images are used to realize lamp post-target extraction. Compared with the traditional visual interpretation, the detection error is 1.45 image elements, which reflects the effectiveness of our method. The generalizability of this method in different types of lamp post targets is verified by increasing the generalizability test.