Extraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height Levels
High density airborne point cloud data have become an important means for modelling and maintenance of power line corridors (PLCs). As the amount of data in a dense point cloud is large, even in a small area, automatic detection of pylon locations can offer a significant advantage by reducing the nu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/15/1798 |
| _version_ | 1828436852775845888 |
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| author | Mohammad Awrangjeb |
| author_facet | Mohammad Awrangjeb |
| author_sort | Mohammad Awrangjeb |
| collection | DOAJ |
| description | High density airborne point cloud data have become an important means for modelling and maintenance of power line corridors (PLCs). As the amount of data in a dense point cloud is large, even in a small area, automatic detection of pylon locations can offer a significant advantage by reducing the number of points that need to be processed in subsequent steps, i.e., the extraction of individual pylons and wires. However, the existing solutions mostly overlook this advantage by processing all of the available data at one time, which hinders their application to large datasets. Moreover, the presence of high vegetation and hilly terrain may challenge many of the existing methods, since vertically overlapping objects (e.g., trees and wires) may not be effectively segmented using a single height threshold. For extraction of pylons and wires, this paper proposes a novel approach which involves converting the input points at different height levels into binary masks. Long straight lines are extracted from these masks and convex hulls around the lines at individual height levels are used to form series of hulls across the height levels. The series of hulls are then projected onto a horizontal plane to form individual corridors. A number of height gaps, where there are no objects between the vegetation and the bottom-most wire, are then estimated. The height gaps along with the height levels consider the presence of hilly terrain as well as high vegetation within the PLCs. By using only the non-ground points within the extracted corridors and height gaps, the pylons are detected. The estimated height gaps are further exploited to define robust seed regions for the detected pylons. The seed regions thereafter are grown to extract the complete pylons. Finally, only the points between the locations of two successive pylons are used to extract points of individual wires. It first counts the number of wires within a power line span and, then, iteratively obtains individual wire points. When tested on two large Australian datasets, the proposed approach exhibited high object-based performance (correctness for pylons and wires of 100% and 99.6%, respectively) and high point-based performance (completeness for pylons and wires of 98.1% and 95%, respectively). Moreover, the planimetric accuracy for the detected pylons was 0.10 m. Thus, the proposed approach is demonstrated to be useful in effective extraction and modelling of pylons and wires. |
| first_indexed | 2024-12-10T19:34:40Z |
| format | Article |
| id | doaj.art-f067062854324e39ba9ecaab41f4a911 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-10T19:34:40Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f067062854324e39ba9ecaab41f4a9112022-12-22T01:36:10ZengMDPI AGRemote Sensing2072-42922019-07-011115179810.3390/rs11151798rs11151798Extraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height LevelsMohammad Awrangjeb0School of Information and Communication Technology, Griffith University, Nathan, QLD 4111, AustraliaHigh density airborne point cloud data have become an important means for modelling and maintenance of power line corridors (PLCs). As the amount of data in a dense point cloud is large, even in a small area, automatic detection of pylon locations can offer a significant advantage by reducing the number of points that need to be processed in subsequent steps, i.e., the extraction of individual pylons and wires. However, the existing solutions mostly overlook this advantage by processing all of the available data at one time, which hinders their application to large datasets. Moreover, the presence of high vegetation and hilly terrain may challenge many of the existing methods, since vertically overlapping objects (e.g., trees and wires) may not be effectively segmented using a single height threshold. For extraction of pylons and wires, this paper proposes a novel approach which involves converting the input points at different height levels into binary masks. Long straight lines are extracted from these masks and convex hulls around the lines at individual height levels are used to form series of hulls across the height levels. The series of hulls are then projected onto a horizontal plane to form individual corridors. A number of height gaps, where there are no objects between the vegetation and the bottom-most wire, are then estimated. The height gaps along with the height levels consider the presence of hilly terrain as well as high vegetation within the PLCs. By using only the non-ground points within the extracted corridors and height gaps, the pylons are detected. The estimated height gaps are further exploited to define robust seed regions for the detected pylons. The seed regions thereafter are grown to extract the complete pylons. Finally, only the points between the locations of two successive pylons are used to extract points of individual wires. It first counts the number of wires within a power line span and, then, iteratively obtains individual wire points. When tested on two large Australian datasets, the proposed approach exhibited high object-based performance (correctness for pylons and wires of 100% and 99.6%, respectively) and high point-based performance (completeness for pylons and wires of 98.1% and 95%, respectively). Moreover, the planimetric accuracy for the detected pylons was 0.10 m. Thus, the proposed approach is demonstrated to be useful in effective extraction and modelling of pylons and wires.https://www.mdpi.com/2072-4292/11/15/1798power linecorridorpylonwireconductordetectionextractionmodelling |
| spellingShingle | Mohammad Awrangjeb Extraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height Levels Remote Sensing power line corridor pylon wire conductor detection extraction modelling |
| title | Extraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height Levels |
| title_full | Extraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height Levels |
| title_fullStr | Extraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height Levels |
| title_full_unstemmed | Extraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height Levels |
| title_short | Extraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height Levels |
| title_sort | extraction of power line pylons and wires using airborne lidar data at different height levels |
| topic | power line corridor pylon wire conductor detection extraction modelling |
| url | https://www.mdpi.com/2072-4292/11/15/1798 |
| work_keys_str_mv | AT mohammadawrangjeb extractionofpowerlinepylonsandwiresusingairbornelidardataatdifferentheightlevels |