# Hierarchical Transmission Tower Detection from High-Resolution SAR Image
## 3 Experiments and Results
### Experimental Results and Analysis
If the number of transmission towers is small, it is easy to cause other false alarm targets that are linearly arranged to be misdetected. In addition, transmission towers are usually erected based on the local geological and environmental conditions, so it is not guaranteed that all transmission towers are kept evenly distributed and equally spaced. This case leads to the low detection rate of the combined CFAR and Contrario algorithm. Furthermore, the deep learning method needs to train a large number of samples, which is time-consuming and labor-intensive. In the case of lack of sufficient samples and few targets in the image, it is easy to lead to low detection accuracy. Moreover, the small size of the transmission tower brings great difficulties to the training results. (2) Among the five algorithms, only the proposed algorithm has a higher accuracy of quality factor. Due to the existence of a large number of false alarm targets, the complexity of the scene increases, such as Figure 7a,c, especially the log-normal distribution of CFAR detection algorithm and the combined CFAR and Contrario algorithm, so the number of false detections increases and subsequently the quality factor decreases to an unsatisfiable accuracy range. In contrast, the proposed algorithm has a small number of false detections, so the quality factor accuracy remains high in all four experimental images. This is not the case for one false alarm in Figure 17(c4), which causes the quality factor to drop to 83.33%. After comparison, it is found that proposed algorithm has the strongest detection ability compared with the other four algorithms, and the identification results for transmission and non-transmission towers are more accurate. Computational complexities are measured by the algorithms' CPU time. Table 3 presents the running time of different algorithms, as shown in Figure 7a. For the comparison algorithms, the Log-normal distribution of CFAR detection algorithm has the shortest running time for the same image size, followed by the proposed algorithm and deep learning method, while the combined CFAR and EF algorithm and the combined CFAR and Contrario algorithm have a relatively longer running time. As the EF algorithm needs to consider both the intensity and geometric features of the target, it takes a longer time to run. Moreover, the Contrario algorithm needs to filter all point targets detected by CFAR detection algorithm, which takes more time. In addition, the combined CFAR and Contrario algorithm has a much greater computational complexity than other algorithms. On the other hand, the hierarchical detection process leads to a longer time for the proposed algorithm to perform the stepped thresholding. However, the computational complexity is much lower than the other four algorithms, and does not need to train a large number of samples. Figure 7:3 Detection results of four images by comparison algorithms. (**a1–d1**) Log-normal distribution of CFAR algorithm. (**a2–d2**) CFAR+EF algorithm [28]. (**a3**)–(**d3**) CFAR + Contrario algorithm [29]. (**a4**)–(**d4**) Proposed algorithm. Figure 7:4 Detection results. (**a–d**) YOLO v5 results. (**e–h**) Proposed algorithm results.