Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone Images
To generate an orthophoto mosaic from a collection of aerial images, the original images are first orthorectified individually using a Digital Surface Model (DSM). Then, they are stitched together along some determined seamlines to form the orthophoto mosaic. Determining appropriate seamlines is a c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/5/903 |
| _version_ | 1797263857134075904 |
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| author | Masood Varshosaz Maryam Sajadian Saied Pirasteh Armin Moghimi |
| author_facet | Masood Varshosaz Maryam Sajadian Saied Pirasteh Armin Moghimi |
| author_sort | Masood Varshosaz |
| collection | DOAJ |
| description | To generate an orthophoto mosaic from a collection of aerial images, the original images are first orthorectified individually using a Digital Surface Model (DSM). Then, they are stitched together along some determined seamlines to form the orthophoto mosaic. Determining appropriate seamlines is a critical process, as it affects the visual and geometric quality of the results. The stitching process can usually be done in frame-to-frame or multi-frame modes. Although the latter is more efficient, both still involve a lot of pre-processing, such as creating individual orthophotos, image registration, and overlap extraction. This paper presents a novel coarse-to-fine approach that directly determines the seamline network without such pre-processing. Our method has been specifically applied for UAV photogrammetry projects where, due to the large number of images and the corresponding overlaps, the orthophoto mosaic generation can be very challenging and time-consuming. We established the seamlines simultaneously for all the images through a two-step process. First, a DSM was generated, and a low-resolution grid was overlayed. Then, for each grid point, an optimal image was selected. Then, the grid cells are grouped into polygons based on their corresponding optimal image. Boundaries of these polygons established our seamline network. Thereafter, to generate the orthophoto mosaic, we overlayed a higher/full resolution grid on the top of the DSM, the optimal image of each point of which was quickly identified via our low-resolution polygons. In this approach, not only seamlines were automatically generated, but also were the need for the creation, registration, and overlap extraction of individual orthophotos. Our method was systematically compared with a conventional frame-to-frame (CF) technique from different aspects, including the number of double-mapped areas, discontinuities across the seamlines network, and the amount of processing time. The outcomes revealed a 46% decrease in orthophoto generation time and a notable reduction in the number of double-mapped areas, sawtooth effects, and object discontinuities within the constructed orthophoto mosaic. |
| first_indexed | 2024-04-25T00:19:40Z |
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| id | doaj.art-1122c8abd7af4fc8b9ff5e9bb9b5d738 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-25T00:19:40Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1122c8abd7af4fc8b9ff5e9bb9b5d7382024-03-12T16:54:23ZengMDPI AGRemote Sensing2072-42922024-03-0116590310.3390/rs16050903Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone ImagesMasood Varshosaz0Maryam Sajadian1Saied Pirasteh2Armin Moghimi3Institute of Artificial Intelligence, Shaoxing University, 508 West Huancheng Road, Yuecheng District, Shaoxing 312000, ChinaGeomatics Engineering Faculty, K.N. Toosi University of Technology, Tehran 19697, IranInstitute of Artificial Intelligence, Shaoxing University, 508 West Huancheng Road, Yuecheng District, Shaoxing 312000, ChinaInstitute of Artificial Intelligence, Shaoxing University, 508 West Huancheng Road, Yuecheng District, Shaoxing 312000, ChinaTo generate an orthophoto mosaic from a collection of aerial images, the original images are first orthorectified individually using a Digital Surface Model (DSM). Then, they are stitched together along some determined seamlines to form the orthophoto mosaic. Determining appropriate seamlines is a critical process, as it affects the visual and geometric quality of the results. The stitching process can usually be done in frame-to-frame or multi-frame modes. Although the latter is more efficient, both still involve a lot of pre-processing, such as creating individual orthophotos, image registration, and overlap extraction. This paper presents a novel coarse-to-fine approach that directly determines the seamline network without such pre-processing. Our method has been specifically applied for UAV photogrammetry projects where, due to the large number of images and the corresponding overlaps, the orthophoto mosaic generation can be very challenging and time-consuming. We established the seamlines simultaneously for all the images through a two-step process. First, a DSM was generated, and a low-resolution grid was overlayed. Then, for each grid point, an optimal image was selected. Then, the grid cells are grouped into polygons based on their corresponding optimal image. Boundaries of these polygons established our seamline network. Thereafter, to generate the orthophoto mosaic, we overlayed a higher/full resolution grid on the top of the DSM, the optimal image of each point of which was quickly identified via our low-resolution polygons. In this approach, not only seamlines were automatically generated, but also were the need for the creation, registration, and overlap extraction of individual orthophotos. Our method was systematically compared with a conventional frame-to-frame (CF) technique from different aspects, including the number of double-mapped areas, discontinuities across the seamlines network, and the amount of processing time. The outcomes revealed a 46% decrease in orthophoto generation time and a notable reduction in the number of double-mapped areas, sawtooth effects, and object discontinuities within the constructed orthophoto mosaic.https://www.mdpi.com/2072-4292/16/5/903orthophoto mosaicdifferential rectificationseamline networkoptimizationdrone imagesDSM |
| spellingShingle | Masood Varshosaz Maryam Sajadian Saied Pirasteh Armin Moghimi Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone Images Remote Sensing orthophoto mosaic differential rectification seamline network optimization drone images DSM |
| title | Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone Images |
| title_full | Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone Images |
| title_fullStr | Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone Images |
| title_full_unstemmed | Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone Images |
| title_short | Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone Images |
| title_sort | automated two step seamline detection for generating large scale orthophoto mosaics from drone images |
| topic | orthophoto mosaic differential rectification seamline network optimization drone images DSM |
| url | https://www.mdpi.com/2072-4292/16/5/903 |
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