Extraction of Roof Feature Lines Based on Geometric Constraints from Airborne LiDAR Data
Airborne LiDAR (Light Detection and Ranging) is an active Earth observing system, which can directly acquire high-accuracy and dense building roof data. Thus, airborne LiDAR has become one of the mainstream source data for building detection and reconstruction. The emphasis for building reconstructi...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/23/5493 |
| _version_ | 1797399613093707776 |
|---|---|
| author | Zhan Cai Hongchao Ma Liang Zhang |
| author_facet | Zhan Cai Hongchao Ma Liang Zhang |
| author_sort | Zhan Cai |
| collection | DOAJ |
| description | Airborne LiDAR (Light Detection and Ranging) is an active Earth observing system, which can directly acquire high-accuracy and dense building roof data. Thus, airborne LiDAR has become one of the mainstream source data for building detection and reconstruction. The emphasis for building reconstruction focuses on the accurate extraction of feature lines. Building roof feature lines generally include the internal and external feature lines. Efficient extraction of these feature lines can provide reliable and accurate information for constructing three-dimensional building models. Most related algorithms adopt intersecting the extracted planes fitted by the corresponding points. However, in these methods, the accuracy of feature lines mostly depends on the results of plane extraction. With the development of airborne LiDAR hardware, the point density is enough for accurate extraction of roof feature lines. Thus, after acquiring the results of building detection, this paper proposed a feature lines extraction strategy based on the geometric characteristics of the original airborne LiDAR data, tracking roof outlines, normal ridge lines, oblique ridge lines and valley lines successively. The final refined feature lines can be obtained by normalization. The experimental results showed that our methods can achieve several promising and reliable results with an accuracy of 0.291 m in the X direction, 0.295 m in the Y direction and 0.091 m in the H direction for outlines extraction. Further, the internal feature lines can be extracted with reliable visual effects using our method. |
| first_indexed | 2024-03-09T01:43:40Z |
| format | Article |
| id | doaj.art-107a0465e47d44ad9cc0eced404f8e88 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T01:43:40Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-107a0465e47d44ad9cc0eced404f8e882023-12-08T15:24:47ZengMDPI AGRemote Sensing2072-42922023-11-011523549310.3390/rs15235493Extraction of Roof Feature Lines Based on Geometric Constraints from Airborne LiDAR DataZhan Cai0Hongchao Ma1Liang Zhang2School of Resources Environment Science and Technology, Hubei University of Science and Technology, Xianning 437100, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaFaculty of Resources and Environmental Science, Hubei University, Wuhan 430062, ChinaAirborne LiDAR (Light Detection and Ranging) is an active Earth observing system, which can directly acquire high-accuracy and dense building roof data. Thus, airborne LiDAR has become one of the mainstream source data for building detection and reconstruction. The emphasis for building reconstruction focuses on the accurate extraction of feature lines. Building roof feature lines generally include the internal and external feature lines. Efficient extraction of these feature lines can provide reliable and accurate information for constructing three-dimensional building models. Most related algorithms adopt intersecting the extracted planes fitted by the corresponding points. However, in these methods, the accuracy of feature lines mostly depends on the results of plane extraction. With the development of airborne LiDAR hardware, the point density is enough for accurate extraction of roof feature lines. Thus, after acquiring the results of building detection, this paper proposed a feature lines extraction strategy based on the geometric characteristics of the original airborne LiDAR data, tracking roof outlines, normal ridge lines, oblique ridge lines and valley lines successively. The final refined feature lines can be obtained by normalization. The experimental results showed that our methods can achieve several promising and reliable results with an accuracy of 0.291 m in the X direction, 0.295 m in the Y direction and 0.091 m in the H direction for outlines extraction. Further, the internal feature lines can be extracted with reliable visual effects using our method.https://www.mdpi.com/2072-4292/15/23/5493airborne LiDARbuilding outlines extractionfeature lines extractiongeometric characteristics of point cloud |
| spellingShingle | Zhan Cai Hongchao Ma Liang Zhang Extraction of Roof Feature Lines Based on Geometric Constraints from Airborne LiDAR Data Remote Sensing airborne LiDAR building outlines extraction feature lines extraction geometric characteristics of point cloud |
| title | Extraction of Roof Feature Lines Based on Geometric Constraints from Airborne LiDAR Data |
| title_full | Extraction of Roof Feature Lines Based on Geometric Constraints from Airborne LiDAR Data |
| title_fullStr | Extraction of Roof Feature Lines Based on Geometric Constraints from Airborne LiDAR Data |
| title_full_unstemmed | Extraction of Roof Feature Lines Based on Geometric Constraints from Airborne LiDAR Data |
| title_short | Extraction of Roof Feature Lines Based on Geometric Constraints from Airborne LiDAR Data |
| title_sort | extraction of roof feature lines based on geometric constraints from airborne lidar data |
| topic | airborne LiDAR building outlines extraction feature lines extraction geometric characteristics of point cloud |
| url | https://www.mdpi.com/2072-4292/15/23/5493 |
| work_keys_str_mv | AT zhancai extractionofrooffeaturelinesbasedongeometricconstraintsfromairbornelidardata AT hongchaoma extractionofrooffeaturelinesbasedongeometricconstraintsfromairbornelidardata AT liangzhang extractionofrooffeaturelinesbasedongeometricconstraintsfromairbornelidardata |