A Novel Method for Digital Orthophoto Generation from Top View Constrained Dense Matching
The digital orthophoto is an image with both map geometric accuracy and image characteristics, which is commonly used in geographic information systems (GIS) as a background image. Existing methods for digital orthophoto generation are generally based on a 3D reconstruction. However, the digital ort...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/1/177 |
| _version_ | 1797431306572791808 |
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| author | Zhihao Zhao Guang Jiang Yunsong Li |
| author_facet | Zhihao Zhao Guang Jiang Yunsong Li |
| author_sort | Zhihao Zhao |
| collection | DOAJ |
| description | The digital orthophoto is an image with both map geometric accuracy and image characteristics, which is commonly used in geographic information systems (GIS) as a background image. Existing methods for digital orthophoto generation are generally based on a 3D reconstruction. However, the digital orthophoto is only the top view of the 3D reconstruction result with a certain spatial resolution. The computation about the surfaces vertical to the ground and details less than the spatial resolution is redundant for digital orthophoto generation. This study presents a novel method for digital orthophoto generation based on top view constrained dense matching (TDM). We first reconstruct some sparse points using the features in the image sequence based on the structure-from-motion (SfM) method. Second, we use a raster to locate the sparse 3D points. Each cell indicates a pixel of the output digital orthophoto. The size of the cell is related to the required spatial resolution. Only some cells with initial values from the sparse 3D points are considered seed cells. The values of other cells around the seed points are computed from a top-down propagation based on color constraints and occlusion detection from multiview-related images. The propagation process continued until the entire raster was occupied. Since the process of TDM is on a raster and only one point is saved in each cell, TDM effectively eliminate the redundant computation. We tested TDM on various scenes and compared it with some commercial software. The experiments showed that our method’s accuracy is the same as the result of commercial software, together with a time consumption decrease as the spatial resolution decreases. |
| first_indexed | 2024-03-09T09:42:01Z |
| format | Article |
| id | doaj.art-c09fddac40de4e7e9e93d3a2d913499a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T09:42:01Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c09fddac40de4e7e9e93d3a2d913499a2023-12-02T00:51:33ZengMDPI AGRemote Sensing2072-42922022-12-0115117710.3390/rs15010177A Novel Method for Digital Orthophoto Generation from Top View Constrained Dense MatchingZhihao Zhao0Guang Jiang1Yunsong Li2School of Telecommunication Engineering, Xidian University, Xi’an 710065, ChinaSchool of Telecommunication Engineering, Xidian University, Xi’an 710065, ChinaSchool of Telecommunication Engineering, Xidian University, Xi’an 710065, ChinaThe digital orthophoto is an image with both map geometric accuracy and image characteristics, which is commonly used in geographic information systems (GIS) as a background image. Existing methods for digital orthophoto generation are generally based on a 3D reconstruction. However, the digital orthophoto is only the top view of the 3D reconstruction result with a certain spatial resolution. The computation about the surfaces vertical to the ground and details less than the spatial resolution is redundant for digital orthophoto generation. This study presents a novel method for digital orthophoto generation based on top view constrained dense matching (TDM). We first reconstruct some sparse points using the features in the image sequence based on the structure-from-motion (SfM) method. Second, we use a raster to locate the sparse 3D points. Each cell indicates a pixel of the output digital orthophoto. The size of the cell is related to the required spatial resolution. Only some cells with initial values from the sparse 3D points are considered seed cells. The values of other cells around the seed points are computed from a top-down propagation based on color constraints and occlusion detection from multiview-related images. The propagation process continued until the entire raster was occupied. Since the process of TDM is on a raster and only one point is saved in each cell, TDM effectively eliminate the redundant computation. We tested TDM on various scenes and compared it with some commercial software. The experiments showed that our method’s accuracy is the same as the result of commercial software, together with a time consumption decrease as the spatial resolution decreases.https://www.mdpi.com/2072-4292/15/1/177digital orthophotostructure-from-motiontop view constrainedtop-down propagationdense matching |
| spellingShingle | Zhihao Zhao Guang Jiang Yunsong Li A Novel Method for Digital Orthophoto Generation from Top View Constrained Dense Matching Remote Sensing digital orthophoto structure-from-motion top view constrained top-down propagation dense matching |
| title | A Novel Method for Digital Orthophoto Generation from Top View Constrained Dense Matching |
| title_full | A Novel Method for Digital Orthophoto Generation from Top View Constrained Dense Matching |
| title_fullStr | A Novel Method for Digital Orthophoto Generation from Top View Constrained Dense Matching |
| title_full_unstemmed | A Novel Method for Digital Orthophoto Generation from Top View Constrained Dense Matching |
| title_short | A Novel Method for Digital Orthophoto Generation from Top View Constrained Dense Matching |
| title_sort | novel method for digital orthophoto generation from top view constrained dense matching |
| topic | digital orthophoto structure-from-motion top view constrained top-down propagation dense matching |
| url | https://www.mdpi.com/2072-4292/15/1/177 |
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