Improvement of 3D Power Line Extraction from Multiple Low-Cost UAV Imagery Using Wavelet Analysis

Abstract

Three-dimensional (3D) mapping of power lines is very important for power line inspection. Many remotely-sensed data products like light detection and ranging (LiDAR) have been already studied for power line surveys. More and more data are being obtained via photogrammetric measurements. This increases the need for the implementation of advanced processing techniques. In recent years, there have been several developments in visualisation techniques using UAV (unmanned aerial vehicle) platform photography. The most modern of such imaging systems have the ability to generate dense point clouds. However, image-based point cloud accuracy is very often various (unstable) and dependent on the radiometric quality of images and the efficiency of image processing algorithms. The main factor influencing the point cloud quality is noise. Such problems usually arise with data obtained via low-cost UAV platforms. Therefore, generated point clouds representing power lines are usually incomplete and noisy. To obtain a complete and accurate 3D model of power lines and towers, it is necessary to develop improved data processing algorithms. The experiment tested the algorithms on power lines with different voltages. This paper presents the wavelet-based method of processing data acquired with a low-cost UAV camera. The proposed, original method involves the application of algorithms for coarse filtration and precise filtering. In addition, a new way of calculating the recommended flight height was proposed. At the end, the accuracy assessment of this two-stage filtration process was examined. For this, point quality indices were proposed. The experimental results show that the proposed algorithm improves the quality of low-cost point clouds. The proposed methods improve the accuracy of determining the parameters of the lines by more than twice. About 10% of noise is reduced by using the wavelet-based approach.

Influence of Lower Atmosphere on the Radiometric Quality of Unmanned Aerial Vehicle Imagery

“…Using UAVs with a compact multispectral digital camera has reduced the cost of acquiring image data [4]. Using UAV photogrammetry, it is possible to quickly obtain extremely high spatial resolution images for topography mapping [1], three-dimensional (3D) modelling [5], or point cloud classification [6,7]. However, studies have not yet considered the broader aspect of the influence of atmospheric and meteorological conditions on the quality of UAV imagery.…”

A Model-Driven Method for Pylon Reconstruction from Oblique UAV Images

“…In addition to the ALS-based pylon reconstruction methods, the related image-based works on power facilities reconstruction are pylon line segment matching [14] and power line reconstruction [1,15]. Hofer et al introduced a line segment matching method to reconstruct the wire objects (e.g., pylons) [14], but the results are only 3D line segments without semantic or topological information.…”

“…An automatic power line measurement method was proposed, which acquired the spatial position of power lines based on epipolar constraint (PLAMEC) and extracted dense point clouds based on semi patch matching. Fryskowska proposed a wavelet-based method to extract the 3D power lines from noisy point clouds generated by image dense matching algorithm [15]. Jiang et al designed an UAV-based oblique photogrammetric system to acquire images of transmission lines and conduct accuracy assessment tests to evaluate and explore the potential application for inspection of transmission lines [3].…”

Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds

“…Zhu et al [22] proposed a method based on statistical analysis and two-dimensional (2D) image processing to automatically extract PLs from ALS data. Fryskowska [34] presented a wavelet-based method to improve the extraction of PLs from low-cost imagery. However, image-based techniques can sometimes produce noisy results [35].…”