# Hierarchical Transmission Tower Detection from High-Resolution SAR Image
## 3 Experiments and Results
### Experimental Results and Analysis
For example, in experimental images shown in Figure 7a,c with relatively complex background, there are a large number of false alarm transmission towers in the detection results, resulting in wrong detection. Compared with the log-normal distribution of CFAR detection algorithm, the false alarm transmission towers detected by the combined CFAR and EF algorithm are significantly reduced, but the EF feature has a weak ability to describe the geometric features of targets. As a result, the detection results cannot effectively distinguish transmission towers from non-transmission towers. The combined CFAR and Contrario algorithm is based on the characteristic of being linearly aligned to detect transmission towers, and it can be seen from Figure 17(a3)-(d3) that the detection results have a large number of false alarm targets, and even the alignments of false alarm targets. Obviously, the algorithm first filters out some candidate points and then detects the transmission towers by using the feature that they present a linear arrangement. However, many false alarm targets can easily be detected by the linear arrangement, and the arrangement of real transmission towers is very demanding, for example, they must be equally spaced and arranged in a straight line, and will be missed if they are turned around. The proposed algorithm not only accurately identifies the transmission towers, but also preserves the geometric characteristics of the transmission towers. Among them, only one false alarm transmission tower appears in Figure 17(c4). Moreover, it can be observed that the size and geometry of the false alarm target is similar to that of the transmission tower, which is the reason why the false alarm target is mistakenly detected as a transmission tower. In addition, in order to verify the superiority of the proposed algorithm, the currently popular deep learning method is used for experimental comparison. As shown in Figure 18a-d, are the detection results of YOLO v5, and Figure 18e-h show the results of the proposed algorithm superimposed on the original images, so as to show the comparison of the results of the two algorithms more intuitively. Observing Figure 18a-d, it can be clearly found that the detection results of the deep learning method are poor, and there are a large number of missed detections and false detections in the four images. The reason is that the detection results are not ideal due to the lack of enough transmission tower training samples and public data sets. Moreover, the small size of the transmission tower makes it difficult to judge its accurate geometry in SAR images with large speckle noise, which is also the reason for the poor results of deep learning methods. To quantitatively evaluate the detection performance of the five  algorithms, this paper uses the detection rate \(\beta_{\text{Rate}}\), quality factor \(\delta_{\text{Quality}}\)  and the number of false detections \(n_{\text{false}}\)  to evaluate them,  
Figure 7:0  
Figure 7:1  
Figure 7:2where \(n_{\text{Correct}}\) is the correct number of detections, \(n_{\text{Real}}\) is the real number of transmission towers, \(n_{\text{missing}}\) is the number of missed detections, and \(n_{\text{false-alarm}}\) is the false alarm target. The higher the detection rate, the better the ability to detect really transmission towers, and the higher the quality factor, the more sensitive the detection algorithm is in discriminating between transmission towers and non-transmission towers. Table 2 presents the detection results of the five algorithms. Comparing the indicators in Table 2, the following conclusions can be drawn: (1) The other three algorithms have high detection rates, except for the combined CFAR and Contrario algorithm and YOLO v5 method. In the experiment, it is found that the combined CFAR and Contrario algorithm is more suitable for detection when the background is simple and the number of transmission towers is large.