A NEW APPROACH OF DIGITAL BRIDGE SURFACE MODEL GENERATION
Bridge areas present difficulties for orthophotos generation and to avoid “collapsed” bridges in the orthoimage, operator assistance is required to create the precise DBM (Digital Bridge Model), which is, subsequently, used for the orthoimage generation. In this paper, a new approach of DBM generati...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2012-07-01
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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/XXXIX-B3/29/2012/isprsarchives-XXXIX-B3-29-2012.pdf |
| _version_ | 1811281517435944960 |
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| author | H. Ju |
| author_facet | H. Ju |
| author_sort | H. Ju |
| collection | DOAJ |
| description | Bridge areas present difficulties for orthophotos generation and to avoid “collapsed” bridges in the orthoimage, operator assistance is required to create the precise DBM (Digital Bridge Model), which is, subsequently, used for the orthoimage generation. In this paper, a new approach of DBM generation, based on fusing LiDAR (Light Detection And Ranging) data and aerial imagery, is proposed. The no precise exterior orientation of the aerial image is required for the DBM generation. First, a coarse DBM is produced from LiDAR data. Then, a robust co-registration between LiDAR intensity and aerial image using the orientation constraint is performed. The from-coarse-to-fine hybrid co-registration approach includes LPFFT (Log-Polar Fast Fourier Transform), Harris Corners, PDF (Probability Density Function) feature descriptor mean-shift matching, and RANSAC (RANdom Sample Consensus) as main components. After that, bridge ROI (Region Of Interest) from LiDAR data domain is projected to the aerial image domain as the ROI in the aerial image. Hough transform linear features are extracted in the aerial image ROI. For the straight bridge, the 1<sup>st</sup> order polynomial function is used; whereas, for the curved bridge, 2<sup>nd</sup> order polynomial function is used to fit those endpoints of Hough linear features. The last step is the transformation of the smooth bridge boundaries from aerial image back to LiDAR data domain and merge them with the coarse DBM. Based on our experiments, this new approach is capable of providing precise DBM which can be further merged with DTM (Digital Terrain Model) derived from LiDAR data to obtain the precise DSM (Digital Surface Model). Such a precise DSM can be used to improve the orthophoto product quality. |
| first_indexed | 2024-04-13T01:34:05Z |
| format | Article |
| id | doaj.art-2b8815b5bfdf449092340ca548140858 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-13T01:34:05Z |
| publishDate | 2012-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-2b8815b5bfdf449092340ca5481408582022-12-22T03:08:25ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342012-07-01XXXIX-B3293410.5194/isprsarchives-XXXIX-B3-29-2012A NEW APPROACH OF DIGITAL BRIDGE SURFACE MODEL GENERATIONH. Ju0Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, USABridge areas present difficulties for orthophotos generation and to avoid “collapsed” bridges in the orthoimage, operator assistance is required to create the precise DBM (Digital Bridge Model), which is, subsequently, used for the orthoimage generation. In this paper, a new approach of DBM generation, based on fusing LiDAR (Light Detection And Ranging) data and aerial imagery, is proposed. The no precise exterior orientation of the aerial image is required for the DBM generation. First, a coarse DBM is produced from LiDAR data. Then, a robust co-registration between LiDAR intensity and aerial image using the orientation constraint is performed. The from-coarse-to-fine hybrid co-registration approach includes LPFFT (Log-Polar Fast Fourier Transform), Harris Corners, PDF (Probability Density Function) feature descriptor mean-shift matching, and RANSAC (RANdom Sample Consensus) as main components. After that, bridge ROI (Region Of Interest) from LiDAR data domain is projected to the aerial image domain as the ROI in the aerial image. Hough transform linear features are extracted in the aerial image ROI. For the straight bridge, the 1<sup>st</sup> order polynomial function is used; whereas, for the curved bridge, 2<sup>nd</sup> order polynomial function is used to fit those endpoints of Hough linear features. The last step is the transformation of the smooth bridge boundaries from aerial image back to LiDAR data domain and merge them with the coarse DBM. Based on our experiments, this new approach is capable of providing precise DBM which can be further merged with DTM (Digital Terrain Model) derived from LiDAR data to obtain the precise DSM (Digital Surface Model). Such a precise DSM can be used to improve the orthophoto product quality.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B3/29/2012/isprsarchives-XXXIX-B3-29-2012.pdf |
| spellingShingle | H. Ju A NEW APPROACH OF DIGITAL BRIDGE SURFACE MODEL GENERATION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | A NEW APPROACH OF DIGITAL BRIDGE SURFACE MODEL GENERATION |
| title_full | A NEW APPROACH OF DIGITAL BRIDGE SURFACE MODEL GENERATION |
| title_fullStr | A NEW APPROACH OF DIGITAL BRIDGE SURFACE MODEL GENERATION |
| title_full_unstemmed | A NEW APPROACH OF DIGITAL BRIDGE SURFACE MODEL GENERATION |
| title_short | A NEW APPROACH OF DIGITAL BRIDGE SURFACE MODEL GENERATION |
| title_sort | new approach of digital bridge surface model generation |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B3/29/2012/isprsarchives-XXXIX-B3-29-2012.pdf |
| work_keys_str_mv | AT hju anewapproachofdigitalbridgesurfacemodelgeneration AT hju newapproachofdigitalbridgesurfacemodelgeneration |