Methodology of Detection and Classification of Selected Aviation Obstacles Based on UAV Dense Image Matching
Currently, more and more accurate data provided by UAVs make it possible to analyze land cover, which requires the detection of objects and their individual elements. Object detection and determination of their geometric features is possible thanks to dense point clouds generated based on imagery ob...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9706249/ |
| _version_ | 1819006045229416448 |
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| author | Marta Lalak Damian Wierzbicki |
| author_facet | Marta Lalak Damian Wierzbicki |
| author_sort | Marta Lalak |
| collection | DOAJ |
| description | Currently, more and more accurate data provided by UAVs make it possible to analyze land cover, which requires the detection of objects and their individual elements. Object detection and determination of their geometric features is possible thanks to dense point clouds generated based on imagery obtained from low altitudes. 3D data from UAVs turn out to be extremely useful for ensuring safety in the airspace in the close vicinity of the airport. This article presents the methodology of automatic aviation obstacle detection based on low altitude data (UAV). The research was carried out on a dense 3D point cloud. The developed methodology for detecting aviation obstacles consists of three main stages. The first is point cloud filtration based on height–preliminary identification of aviation obstacles, followed by 3D point cloud segmentation using a modified RANSAC algorithm, supplemented with two-dimensional vector data of aviation obstacles to improve the accuracy of the segmentation process. The last stage is the classification of aviation obstacles according to the adopted height and cross-section criterion. The proposed method of detecting aviation obstacles is characterized by high accuracy. The mean error of fitting the point cloud to the obstacle database ranged from ± 0.04 m to ± 0.07 m. |
| first_indexed | 2024-12-21T00:02:26Z |
| format | Article |
| id | doaj.art-29ae270dab474e6cb1b7eb00d023a75f |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-21T00:02:26Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-29ae270dab474e6cb1b7eb00d023a75f2022-12-21T19:22:35ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-01151869188310.1109/JSTARS.2022.31491059706249Methodology of Detection and Classification of Selected Aviation Obstacles Based on UAV Dense Image MatchingMarta Lalak0https://orcid.org/0000-0001-5485-4720Damian Wierzbicki1https://orcid.org/0000-0001-6192-3894Institute of Navigation, Polish Air Force University, Dęblin, PolandDepartment of Imagery Intelligence, Faculty of Civil Engineering and Geodesy, Military University of Technology, Warsaw, PolandCurrently, more and more accurate data provided by UAVs make it possible to analyze land cover, which requires the detection of objects and their individual elements. Object detection and determination of their geometric features is possible thanks to dense point clouds generated based on imagery obtained from low altitudes. 3D data from UAVs turn out to be extremely useful for ensuring safety in the airspace in the close vicinity of the airport. This article presents the methodology of automatic aviation obstacle detection based on low altitude data (UAV). The research was carried out on a dense 3D point cloud. The developed methodology for detecting aviation obstacles consists of three main stages. The first is point cloud filtration based on height–preliminary identification of aviation obstacles, followed by 3D point cloud segmentation using a modified RANSAC algorithm, supplemented with two-dimensional vector data of aviation obstacles to improve the accuracy of the segmentation process. The last stage is the classification of aviation obstacles according to the adopted height and cross-section criterion. The proposed method of detecting aviation obstacles is characterized by high accuracy. The mean error of fitting the point cloud to the obstacle database ranged from ± 0.04 m to ± 0.07 m.https://ieeexplore.ieee.org/document/9706249/Accuracyair traffic controlimage processingremote sensingunmanned aerial vehicles (UAVs) |
| spellingShingle | Marta Lalak Damian Wierzbicki Methodology of Detection and Classification of Selected Aviation Obstacles Based on UAV Dense Image Matching IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Accuracy air traffic control image processing remote sensing unmanned aerial vehicles (UAVs) |
| title | Methodology of Detection and Classification of Selected Aviation Obstacles Based on UAV Dense Image Matching |
| title_full | Methodology of Detection and Classification of Selected Aviation Obstacles Based on UAV Dense Image Matching |
| title_fullStr | Methodology of Detection and Classification of Selected Aviation Obstacles Based on UAV Dense Image Matching |
| title_full_unstemmed | Methodology of Detection and Classification of Selected Aviation Obstacles Based on UAV Dense Image Matching |
| title_short | Methodology of Detection and Classification of Selected Aviation Obstacles Based on UAV Dense Image Matching |
| title_sort | methodology of detection and classification of selected aviation obstacles based on uav dense image matching |
| topic | Accuracy air traffic control image processing remote sensing unmanned aerial vehicles (UAVs) |
| url | https://ieeexplore.ieee.org/document/9706249/ |
| work_keys_str_mv | AT martalalak methodologyofdetectionandclassificationofselectedaviationobstaclesbasedonuavdenseimagematching AT damianwierzbicki methodologyofdetectionandclassificationofselectedaviationobstaclesbasedonuavdenseimagematching |