POINT CLOUD AND DIGITAL SURFACE MODEL GENERATION FROM HIGH RESOLUTION MULTIPLE VIEW STEREO SATELLITE IMAGERY
Nowadays, multiple-view stereo satellite imagery has become a valuable data source for digital surface model generation and 3D reconstruction. In 2016, a well-organized multiple view stereo publicly benchmark for commercial satellite imagery has been released by the John Hopkins University Applied P...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-05-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/363/2018/isprs-archives-XLII-2-363-2018.pdf |
| _version_ | 1811327976102428672 |
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| author | K. Gong D. Fritsch |
| author_facet | K. Gong D. Fritsch |
| author_sort | K. Gong |
| collection | DOAJ |
| description | Nowadays, multiple-view stereo satellite imagery has become a valuable data source for digital surface model generation and 3D reconstruction. In 2016, a well-organized multiple view stereo publicly benchmark for commercial satellite imagery has been released by the John Hopkins University Applied Physics Laboratory, USA. This benchmark motivates us to explore the method that can generate accurate digital surface models from a large number of high resolution satellite images. In this paper, we propose a pipeline for processing the benchmark data to digital surface models. As a pre-procedure, we filter all the possible image pairs according to the incidence angle and capture date. With the selected image pairs, the relative bias-compensated model is applied for relative orientation. After the epipolar image pairs’ generation, dense image matching and triangulation, the 3D point clouds and DSMs are acquired. The DSMs are aligned to a quasi-ground plane by the relative bias-compensated model. We apply the median filter to generate the fused point cloud and DSM. By comparing with the reference LiDAR DSM, the accuracy, the completeness and the robustness are evaluated. The results show, that the point cloud reconstructs the surface with small structures and the fused DSM generated by our pipeline is accurate and robust. |
| first_indexed | 2024-04-13T15:18:32Z |
| format | Article |
| id | doaj.art-0d11b9327c694c5f994e1e4fe4be98d5 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-13T15:18:32Z |
| publishDate | 2018-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-0d11b9327c694c5f994e1e4fe4be98d52022-12-22T02:41:46ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342018-05-01XLII-236337010.5194/isprs-archives-XLII-2-363-2018POINT CLOUD AND DIGITAL SURFACE MODEL GENERATION FROM HIGH RESOLUTION MULTIPLE VIEW STEREO SATELLITE IMAGERYK. Gong0D. Fritsch1Institute for Photogrammetry, University Stuttgart, 70174 Stuttgart, GermanyInstitute for Photogrammetry, University Stuttgart, 70174 Stuttgart, GermanyNowadays, multiple-view stereo satellite imagery has become a valuable data source for digital surface model generation and 3D reconstruction. In 2016, a well-organized multiple view stereo publicly benchmark for commercial satellite imagery has been released by the John Hopkins University Applied Physics Laboratory, USA. This benchmark motivates us to explore the method that can generate accurate digital surface models from a large number of high resolution satellite images. In this paper, we propose a pipeline for processing the benchmark data to digital surface models. As a pre-procedure, we filter all the possible image pairs according to the incidence angle and capture date. With the selected image pairs, the relative bias-compensated model is applied for relative orientation. After the epipolar image pairs’ generation, dense image matching and triangulation, the 3D point clouds and DSMs are acquired. The DSMs are aligned to a quasi-ground plane by the relative bias-compensated model. We apply the median filter to generate the fused point cloud and DSM. By comparing with the reference LiDAR DSM, the accuracy, the completeness and the robustness are evaluated. The results show, that the point cloud reconstructs the surface with small structures and the fused DSM generated by our pipeline is accurate and robust.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/363/2018/isprs-archives-XLII-2-363-2018.pdf |
| spellingShingle | K. Gong D. Fritsch POINT CLOUD AND DIGITAL SURFACE MODEL GENERATION FROM HIGH RESOLUTION MULTIPLE VIEW STEREO SATELLITE IMAGERY The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | POINT CLOUD AND DIGITAL SURFACE MODEL GENERATION FROM HIGH RESOLUTION MULTIPLE VIEW STEREO SATELLITE IMAGERY |
| title_full | POINT CLOUD AND DIGITAL SURFACE MODEL GENERATION FROM HIGH RESOLUTION MULTIPLE VIEW STEREO SATELLITE IMAGERY |
| title_fullStr | POINT CLOUD AND DIGITAL SURFACE MODEL GENERATION FROM HIGH RESOLUTION MULTIPLE VIEW STEREO SATELLITE IMAGERY |
| title_full_unstemmed | POINT CLOUD AND DIGITAL SURFACE MODEL GENERATION FROM HIGH RESOLUTION MULTIPLE VIEW STEREO SATELLITE IMAGERY |
| title_short | POINT CLOUD AND DIGITAL SURFACE MODEL GENERATION FROM HIGH RESOLUTION MULTIPLE VIEW STEREO SATELLITE IMAGERY |
| title_sort | point cloud and digital surface model generation from high resolution multiple view stereo satellite imagery |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/363/2018/isprs-archives-XLII-2-363-2018.pdf |
| work_keys_str_mv | AT kgong pointcloudanddigitalsurfacemodelgenerationfromhighresolutionmultipleviewstereosatelliteimagery AT dfritsch pointcloudanddigitalsurfacemodelgenerationfromhighresolutionmultipleviewstereosatelliteimagery |