Using ALS Data to Improve Co-Registration of Photogrammetry-Based Point Cloud Data in Urban Areas
Globally, urban areas are rapidly expanding and high-quality remote sensing products are essential to help guide such development towards efficient and sustainable pathways. Here, we present an algorithm to address a common problem in digital aerial photogrammetry (DAP)-based image point clouds: ver...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/12/1943 |
| _version_ | 1797565105865490432 |
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| author | Ranjith Gopalakrishnan Daniela Ali-Sisto Mikko Kukkonen Pekka Savolainen Petteri Packalen |
| author_facet | Ranjith Gopalakrishnan Daniela Ali-Sisto Mikko Kukkonen Pekka Savolainen Petteri Packalen |
| author_sort | Ranjith Gopalakrishnan |
| collection | DOAJ |
| description | Globally, urban areas are rapidly expanding and high-quality remote sensing products are essential to help guide such development towards efficient and sustainable pathways. Here, we present an algorithm to address a common problem in digital aerial photogrammetry (DAP)-based image point clouds: vertical mis-registration. The algorithm uses the ground as inferred from airborne laser scanning (ALS) data as a reference surface and re-aligns individual point clouds to this surface. We demonstrate the effectiveness of the proposed method for the city of Kuopio, in central Finland. Here, we use the standard deviation of the vertical coordinate values as a measure of the mis-registration. We show that such standard deviation decreased substantially (more than 1.0 m) for a large proportion (23.2%) of the study area. Moreover, it was shown that the method performed better in urban and suburban areas, compared to vegetated areas (parks, forested areas, and so on). Hence, we demonstrate that the proposed algorithm is a simple and effective method to improve the quality and usability of DAP-based point clouds in urban areas. |
| first_indexed | 2024-03-10T19:07:16Z |
| format | Article |
| id | doaj.art-90a9e0c7e2bd4264a7dd6d33d90d3452 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:07:16Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-90a9e0c7e2bd4264a7dd6d33d90d34522023-11-20T04:02:53ZengMDPI AGRemote Sensing2072-42922020-06-011212194310.3390/rs12121943Using ALS Data to Improve Co-Registration of Photogrammetry-Based Point Cloud Data in Urban AreasRanjith Gopalakrishnan0Daniela Ali-Sisto1Mikko Kukkonen2Pekka Savolainen3Petteri Packalen4School of Forest Sciences, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, FinlandSchool of Forest Sciences, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, FinlandSchool of Forest Sciences, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, FinlandTerraTec Oy, Karjalankatu 2, 00520 Helsinki, FinlandSchool of Forest Sciences, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, FinlandGlobally, urban areas are rapidly expanding and high-quality remote sensing products are essential to help guide such development towards efficient and sustainable pathways. Here, we present an algorithm to address a common problem in digital aerial photogrammetry (DAP)-based image point clouds: vertical mis-registration. The algorithm uses the ground as inferred from airborne laser scanning (ALS) data as a reference surface and re-aligns individual point clouds to this surface. We demonstrate the effectiveness of the proposed method for the city of Kuopio, in central Finland. Here, we use the standard deviation of the vertical coordinate values as a measure of the mis-registration. We show that such standard deviation decreased substantially (more than 1.0 m) for a large proportion (23.2%) of the study area. Moreover, it was shown that the method performed better in urban and suburban areas, compared to vegetated areas (parks, forested areas, and so on). Hence, we demonstrate that the proposed algorithm is a simple and effective method to improve the quality and usability of DAP-based point clouds in urban areas.https://www.mdpi.com/2072-4292/12/12/1943height adjustmentco-registrationdigital aerial photogrammetry (DAP)Urban environmentaerial imagingairborne laser scanning |
| spellingShingle | Ranjith Gopalakrishnan Daniela Ali-Sisto Mikko Kukkonen Pekka Savolainen Petteri Packalen Using ALS Data to Improve Co-Registration of Photogrammetry-Based Point Cloud Data in Urban Areas Remote Sensing height adjustment co-registration digital aerial photogrammetry (DAP) Urban environment aerial imaging airborne laser scanning |
| title | Using ALS Data to Improve Co-Registration of Photogrammetry-Based Point Cloud Data in Urban Areas |
| title_full | Using ALS Data to Improve Co-Registration of Photogrammetry-Based Point Cloud Data in Urban Areas |
| title_fullStr | Using ALS Data to Improve Co-Registration of Photogrammetry-Based Point Cloud Data in Urban Areas |
| title_full_unstemmed | Using ALS Data to Improve Co-Registration of Photogrammetry-Based Point Cloud Data in Urban Areas |
| title_short | Using ALS Data to Improve Co-Registration of Photogrammetry-Based Point Cloud Data in Urban Areas |
| title_sort | using als data to improve co registration of photogrammetry based point cloud data in urban areas |
| topic | height adjustment co-registration digital aerial photogrammetry (DAP) Urban environment aerial imaging airborne laser scanning |
| url | https://www.mdpi.com/2072-4292/12/12/1943 |
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