3D Power Line Extraction from Multiple Aerial Images

Oh, Jaehong; Lee, Changno

doi:10.3390/s17102244

Cited by 18 publications

(19 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scale j = 2 was also found as less detailed providing less geometric information. Therefore, the scales j = [1,2,6] were all shrunken to zero. The remaining coefficients at scales j = [3,4,5] were found containing significant power line information (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Satellites and aerial vehicles provide cheaper and fast ways of inspecting them. They allow capturing the images of the power lines remotely [2]. They provide potentials for automatic extraction of the power lines so as to derive fast and up-to-date information.…”

Section: Introductionmentioning

confidence: 99%

“…In [13] epipolar constraints based power line automatic measurement and semi patch matching methods were used in extracting 2D and 3D power lines from epipolar images. A 3D power lines extraction method from aerial images was also proposed using photogrammetric parameters, filtering and cubic grid points [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Integrated Multiscale Geometric Analysis Approach for Automatic Extraction of Power Lines From High Resolution Remote Sensing Images

Feyissa

Cao

2020

IEEE Access

View full text Add to dashboard Cite

High resolution remote sensing systems provide cheaper and fast way of acquiring images of power lines. However, such images depicting the details of other complex background objects, noises, and complicated brightness measurements, make separate extraction of the power lines challenging. This paper addresses the problem of automatic extraction of power lines from high resolution remote sensing images obtained from different sources. In order to automatically extract the power lines, we proposed an integrated Multiscale Geometric Analysis (MGA) approach. First, complementary Gabor and matched filters (MF) were employed over an image to suppress unnecessary background and noises, and initial discrimination of the power lines. Then, the filtering output was decomposed in to scale and orientation based subband coefficients using the Fast Discrete Curvelet Transform (FDCT) so as to access and modify different image features separately. By employing selective modification operations, well-established power line structures ready for extraction were derived. Finally the powerlines were extracted with hysteresis thresholding. The approach was successful in extracting power lines from high resolution images captured in any orientation. It is robust even when the source image is cluttered, and degraded due to noise and brightness effects. Power lines represented by weak intensities, crossing bright image regions, changing direction, closer power lines and those crossing each other, disconnected/broken power lines due to noise and occlusions were all inferred and extracted successfully. The approach was validated using real test images and the performance measures showed over 90% average accuracy fitting the ground truth.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Integrated Multiscale Geometric Analysis Approach for Automatic Extraction of Power Lines From High Resolution Remote Sensing Images

Feyissa

Cao

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…For all these cases, the other methods RANSAC, Hough transformation and statistical methods were also introduced in [9,10]. Recently, compact and lightweight UAV-LiDAR sensors were introduced into the market, although their performance is limited in terms of scan speed and measurement rate [11]. Ax et al in [12] presented the use of laser scanner data from a UAV helicopter for vegetation control at high-voltage transmission lines; Teng et al in [13] proposed their own LiDAR-UAV system.…”

Section: Introductionmentioning

confidence: 99%

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

Fryśkowska

2019

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…However, due to the small size of utility distribution poles, the spatial resolution of most satellite platforms is not sufficient for reliable detection [ 4 , 21 ]. The ultra-high spatial resolutions provided by UAV platforms have made them an emerging tool for surveying electric utilities [ 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images

Zhang

Witharana

et al. 2018

Sensors

View full text Add to dashboard Cite

Traditional methods of detecting and mapping utility poles are inefficient and costly because of the demand for visual interpretation with quality data sources or intense field inspection. The advent of deep learning for object detection provides an opportunity for detecting utility poles from side-view optical images. In this study, we proposed using a deep learning-based method for automatically mapping roadside utility poles with crossarms (UPCs) from Google Street View (GSV) images. The method combines the state-of-the-art DL object detection algorithm (i.e., the RetinaNet object detection algorithm) and a modified brute-force-based line-of-bearing (LOB, a LOB stands for the ray towards the location of the target [UPC at here] from the original location of the sensor [GSV mobile platform]) measurement method to estimate the locations of detected roadside UPCs from GSV. Experimental results indicate that: (1) both the average precision (AP) and the overall accuracy (OA) are around 0.78 when the intersection-over-union (IoU) threshold is greater than 0.3, based on the testing of 500 GSV images with a total number of 937 objects; and (2) around 2.6%, 47%, and 79% of estimated locations of utility poles are within 1 m, 5 m, and 10 m buffer zones, respectively, around the referenced locations of utility poles. In general, this study indicates that even in a complex background, most utility poles can be detected with the use of DL, and the LOB measurement method can estimate the locations of most UPCs.

show abstract

3D Power Line Extraction from Multiple Aerial Images

Cited by 18 publications

References 13 publications

An Integrated Multiscale Geometric Analysis Approach for Automatic Extraction of Power Lines From High Resolution Remote Sensing Images

An Integrated Multiscale Geometric Analysis Approach for Automatic Extraction of Power Lines From High Resolution Remote Sensing Images

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

Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images

Contact Info

Product

Resources

About