# Hierarchical Transmission Tower Detection from High-Resolution SAR Image
## 1 Introduction
Subsequently, based on the characteristics of transmission towers. [PERSON] et al. [29] proposed an algorithm to detect transmission towers using contrario theory. However, the algorithm is limited and needs to satisfy the equidistant linear arrangement, and the wrong detection is more serious. In addition, as an important research method of target detection, deep learning is also sought after by many scholars. Such as, Region-based CNNs (R-CNN) [30], Faster-RCNN [31], YOLO (You Only Look Once) [32] and SSD (Single Shot Multibox) [33] have been developed in the recent years for object detection tasks [26]. However, as the depth of the network increases, the convolutional neural network often encounters some negative effects, such as overfitting and gradient explosion, which affect the performance of the network itself. To this end, [PERSON] et al. [34] proposed a deep cascaded network for change detection in SAR images. In addition, it is well known that speckle noise in SAR images has a great impact on detection. [PERSON] et al. [35] proposed a two-stage target-based deep learning method for multi-temporal SAR image change detection. Although this method reduced the false alarm rate to a certain extent, it can be found from the results that there was a loss of spatial details, over-reliance on the characteristics of deep learning, can only handle specialized tasks, and was not applicable to other complex tasks. Apparently, deep learning methods are mainly used for transmission detection by training a large number of transmission tower samples. [PERSON] et al. [36] used Polar Coordinate Semi-Variogram (PCSV) to extract the geometric features of Region of Interest (ROI) and transmission tower detection is implemented by using a three-layer neural network. However, the algorithm requiresextensive preprocessing before the experiment, resulting in the detection of a large number of false alarm targets with similar geometric features to the transmission towers. [PERSON] et al. [4] proposed an improved SSD-based transmission tower detection method to speed up the training by reducing the categories in the dataset and changing the scale and aspect ratio of default boxes in SSD, which improves the detection speed but tends to reduce the detection accuracy. In summary, from the results and basic ideas of these proposed algorithms, for large-scale images, all of them extract transmission towers as point targets, and the detection results have high false alarm rates or improve the detection rate through the idea of image enhancement and noise suppression [37]. Additionally, the algorithms that consider the geometric shape features of transmission towers ignore the amplitude information of transmission towers in SAR images. This paper proposes a hierarchical algorithm from high-resolution SAR images. First, the Signal-to-Clutter Ratio (SCR) [38] features of the transmission towers in SAR images are used to extract the potential transmission tower pixels, which is different from the above literature that constructs an a priori model to detect the transmission towers. Secondly, based on the aggregation characteristics of transmission tower pixels, some potential transmission tower pixels with small spatial density are eliminated, and the remaining potential transmission tower pixels are considered as candidate transmission tower pixels. Finally, the convex-hulls [39] of quasi-transmission towers are generated by calculating the minimum distance between the candidate transmission tower pixel pairs, grouping the quasi-transmission tower pixel groups, and generating their minimum bounding rectangles (MBRs) [40]. The MBR aspect ratio extraction criterion is set according to the actual size of the transmission tower and its imaging morphology in the SAR images to complete the accurate extraction of transmission tower. Fast, robust and effective detection of transmission towers is realized to meet the detection needs of transmission towers. The remainder of this paper is organized as follows.