# Hierarchical Transmission Tower Detection from High-Resolution SAR Image
## 3 Experiments and Results
### Experimental Results and Analysis  
To validate the feasibility and effectiveness of the proposed algorithm, transmission towers detection experiments in real SAR remote sensing images are performed on a PC with Intel (R) Core (TM) i5-4460, 3.20 GHz and 8 GB memory using MATLAB R2020b. Figure 8 shows the normalized _SCR_ image results, with white pixels the high SCR region and black pixels the low SCR region. As seen in Figure 8, the detail information within the SCR image is stronger than that of the original image. Although the transmission tower is hidden in a large amount of background clutter, it can be seen using Formula (1) that the proposed algorithm fully considers the relationship between transmission tower pixels and neighboring pixels, and has a stronger SCR value compared to other white background pixel clutter. Figure 9a-d show the histograms of the _SCR_ images corresponding to Figure 8a-d, respectively, and fitted Gaussian distribution curves (in red) to the histograms. According to Formula (3), the SCR thresholds can be calculated as 5.70, 4.63, 6.32 and 5.72 for the images shown in Figure 9a-d, respectively. Figure 8: _SCR_ images. (a–d) represent the _SCR_ images transformed from Figure 7, respectively. Figure 7: Experimental SAR images. (a) farmland with 8m resolution, located in Shenyang City, China, and contains five transmission towers (b–d) show farmland and wasteland with 3 m resolution, located in Fuxin City, China, and contain three, five and nine transmission towers, respectively. Figure 10a-d show the potential transmission tower pixels by thresholding the SCR images in Figure 8a-d, respectively. It can be seen from Figure 10a-d, for each transmission tower, there are always part of its pixels being detected, although there are still some false alarm pixels in the potential transmission tower pixels. Figure 11a-h show the results using gray threshold method with different threshold in Figure 7a-d, respectively. Comparing the detection results of the two methods, it is found that the detection using SCR images is more advantageous compared to the original images. A large number of background interference pixels are removed while ensuring that transmission towers are not missed, and the geometry of transmission tower pixels is preserved. Taking the transmission tower in Figure 10a as an example, a red box is used to mark the corresponding location of the transmission tower and it is zoomed in to show that the pixel distribution and geometry of the transmission tower can be clearly observed without losing important component pixels. However, when the threshold is increased, most of the transmission tower pixels are filtered out, leaving only a few main pixels, as shown in Figure 11e-h. Among them, by observing Figure 11f, it can be found that the geometry of the transmission tower cannot be identified, and a transmission tower is missed in Figure 11h. Therefore, a comparison with Figure 11 indicates that the proposed algorithm is not simply and irregularly filtered by the amplitude values. Figure 9: _SCR_ images histograms fitting. (**a–d**) represent the histograms fitting results of SCR images in Figure 8, respectively. By calculating the spatial densities of potential transmission tower pixels, candidate transmission tower pixels can be obtained by thresholding the densities. Observing the detection results in Figure 10, the transmission tower pixels have obvious spatial aggregation. According to Formula (5), the density threshold ranges of Figure 10a-d are set to 18-66. Therefore, Figure 12a-d show the candidate transmission tower pixels by thresholding the densities of potential transmission tower pixels in Figure 10a-d, respectively. Observing Figure 12, it can be noticed that most of the false alarm pixels have been filtered out and only a few pixels are detected as candidates for the number of transmission tower pixels.