# Hierarchical Transmission Tower Detection from High-Resolution SAR Image
## 1 Introduction  
As protection and support equipment for overhead transmission lines, transmission towers are important infrastructure for the power industry [1]. They are situated in a wide variety of natural environments such as the suburbs, farmland, forests and other field areas, and are very vulnerable to various natural disasters such as floods, landslide, wildlife, wind damage and so on [2]. To ensure the uninterrupted and safe transmission of electricity, effective detection and maintenance of transmission towers are essential [3, 4]. Initially, transmission towers are mostly monitored by unmanned aerial vehicles (UAV) [5, 6] and helicopter patrols to collect information about the status of transmission towers. Obviously, these methods consume a lot of manpower, material and financial resources and are prone to measurement errors. Therefore, the transmission tower detection based on remote sensing images is developed. Transmission towers are symmetrical in structure, unchanged for a long time and numerous in number. However, they are vertical structures thatoccupy a small area, so it is often difficult to detect transmission towers in remote sensing images. Synthetic Aperture Radar (SAR) system [7; 8] can effectively avoid the influence of cloud, fog, rain, snow and other climatic conditions, and can continuously observe the ground surface for a long time [9; 10], so it is widely applied in various fields such as target detection [11; 12] and disaster monitoring [13; 14]. However, SAR images have strong speckle noise [15; 16; 17], which increases the difficulty of the object detection task. With the continuous improvement of SAR system resolution, the information of transmission towers in the images becomes more and more abundant, so the detection of transmission towers from SAR images has gradually received the attention of many researchers [18]. Over the past decades, numerous transmission tower detection algorithms have been proposed. Depending on the data types, they are mainly divided into airborne or spaceborne SAR-based. In the early transmission tower detection, airborne SAR images are mainly used. For example, [PERSON] et al. [19] proposed to construct Digital Surface Models (DSMs) using airborne InSAR [20] images and to detect vertical obstacles such as various towers based on the DSMs by modeling their geometries. However, this algorithm did not fully utilize the spectral information within the SAR images. [PERSON] et al. [21] proposed an algorithm for automatic detection of transmission towers based on airborne fully polarized SAR images. This algorithm analyzes the four-component scattering model [22] and the spiral scattering component separately, extracts the point targets, and then connects all the isolated points as the final transmission tower lines. Currently, it is becoming a trend that spaceborne SAR images are used to detect transmission towers. [PERSON] et al. [23] used the TerraSAR-X SSC images to detect and estimate the height of transmission towers. From the detection results of these algorithms, there still exist large false alarms. Depending on the cardinal principles used, transmission tower detection methods can be divided into Constant False Alarm Rate (CFAR) [24; 25] methods and deep learning methods [4; 24; 26]. For transmission tower detection in SAR images, the CFAR algorithm is one of the most commonly used algorithms, which has been used and studied for many years due to its computational simplicity and stable performance. For example, [PERSON] et al. [27] proposed a local CFAR-based detection model to reconstruct transmission towers by local CFAR model, extract scale parameters, and eliminate coherent scattering noise to implement the detection algorithm. Therefore, [PERSON] et al. [28] combined CFAR with Extended Fractal (EF) to detect transmission towers as point targets in SAR intensity images. However, EF features have limited ability to describe geometric features, resulting in less accurate detection results.