# Hierarchical Transmission Tower Detection from High-Resolution SAR Image
## 3 Experiments and Results
### Experimental Results and Analysis
Additionally, all the transmission tower main pixels within the four images have also been retained, which verifies the effectiveness of the density thresholding algorithm. Figure 11: Results of the gray threshold method. (**a–d**) show the results with the threshold set to 150. (**e–h**) show the results with the threshold set to 180. The red box is used to zoom in the details of the transmission tower. Figure 10: Results of SCR threshold. (**a–d**) represent the results of SCR threshold based on Figure 8, respectively. The red box is used to zoom in the details of the transmission tower. The results of grouping the candidate transmission tower pixels by the nearest neighbor scheme are shown in Figure 13a-d. By calculating the distance between pixel pairs, several sets of pixels are determined and each set of pixels is considered as a quasi-transmission tower. Figure 13a-d contain 7, 3, 6 and 11 quasi-transmission towers, respectively. Figure 14a-d show the results of MBRs extraction for each quasi-transmission tower. The details of each transmission tower from Figure 14a-d are shown by zooming in, which can qualitatively verify that proposed algorithm can detect the transmission towers relatively effectively and preserve their geometric features. The aspect ratio of each quasi-transmission tower is obtained by extracting the MBR of the quasi-transmission tower, and the results are shown in Figure 14a,c as examples  
Figure 14: Results of the construction of MBRs. (**a–d**) show the MBRs of each transmission tower in red and show the details of the MBRs of the transmission tower in red boxes. Figure 12: Detection results of spatial density threshold. (**a–d**) show the results of spatial density threshold based on Figure 10, respectively. Figure 13: Results of the nearest neighbor scheme for candidate transmission tower. (**a–d**) represent the candidate transmission tower results from Figure 12.  
and are listed in Table 1. (It is worth noting that in this paper, based on the imaging characteristics of transmission towers, the default horizontal axis is the length and the vertical axis is the width.) Among them, the aspect ratio thresholds of Figure 14a-d are set to 1.0, 3.5, 1.2, 3.5, respectively. Therefore, the non-transmission towers are further filtered to obtain the real transmission tower detection results, as shown in Figure 15a-d, and the real and the false alarm transmission towers are marked with red rectangles and yellow triangles, respectively. Observing Figure 15a-d, most of the false alarm targets are eliminated, and it can be seen that the detection results do not show any missed detection, only Figure 15c shows one false alarm. Overall, as expected, the proposed algorithm can detect the transmission towers well, so as to better improve the detection rates. Figure 16 shows the effect of superimposing the final transmission tower detection results onto the original image, which can better show the detection effect of the proposed algorithm. Figure 17 shows results from the comparison algorithms for transmission towers detection in Figure 7a-d. Among them, Figure 17(a1)-(d1) show the results of Log-normal distribution of CFAR detection algorithm. Figure 17(a2)-(d2) show the results of the combined CFAR and EF algorithm. Figure 17(a3)-(d3) show the results of and the combined CFAR and Contrario algorithm. Additionally, Figure 17(a4)-(d4) show the results of the proposed algorithm. In Figure 17, the red and yellow rectangle marks the real and false alarm transmission towers, respectively, and the green oval marks the missed detection transmission towers. As seen from the first row of Figure 17, the log-normal distribution of CFAR detection algorithm is simple and can describe the asymmetrical distribution of tailing phenomenon, it is not suitable for the identification of small targets under complex scenes.