An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images
Recently, the mapping industry has been focusing on the possibility of large-scale mapping from unmanned aerial vehicles (UAVs) owing to advantages such as easy operation and cost reduction. In order to produce large-scale maps from UAV images, it is important to obtain precise orientation parameter...
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| Format: | Article |
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MDPI AG
2021-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/11/2118 |
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| author | Pyung-chae Lim Sooahm Rhee Junghoon Seo Jae-In Kim Junhwa Chi Suk-bae Lee Taejung Kim |
| author_facet | Pyung-chae Lim Sooahm Rhee Junghoon Seo Jae-In Kim Junhwa Chi Suk-bae Lee Taejung Kim |
| author_sort | Pyung-chae Lim |
| collection | DOAJ |
| description | Recently, the mapping industry has been focusing on the possibility of large-scale mapping from unmanned aerial vehicles (UAVs) owing to advantages such as easy operation and cost reduction. In order to produce large-scale maps from UAV images, it is important to obtain precise orientation parameters as well as analyzing the sharpness of they themselves measured through image analysis. For this, various techniques have been developed and are included in most of the commercial UAV image processing software. For mapping, it is equally important to select images that can cover a region of interest (ROI) with the fewest possible images. Otherwise, to map the ROI, one may have to handle too many images, and commercial software does not provide information needed to select images, nor does it explicitly explain how to select images for mapping. For these reasons, stereo mapping of UAV images in particular is time consuming and costly. In order to solve these problems, this study proposes a method to select images intelligently. We can select a minimum number of image pairs to cover the ROI with the fewest possible images. We can also select optimal image pairs to cover the ROI with the most accurate stereo pairs. We group images by strips and generate the initial image pairs. We then apply an intelligent scheme to iteratively select optimal image pairs from the start to the end of an image strip. According to the results of the experiment, the number of images selected is greatly reduced by applying the proposed optimal image–composition algorithm. The selected image pairs produce a dense 3D point cloud over the ROI without any holes. For stereoscopic plotting, the selected image pairs were map the ROI successfully on a digital photogrammetric workstation (DPW) and a digital map covering the ROI is generated. The proposed method should contribute to time and cost reductions in UAV mapping. |
| first_indexed | 2024-03-10T10:57:04Z |
| format | Article |
| id | doaj.art-ee4743b9214f4f40827c9eb2e4bc5723 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T10:57:04Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ee4743b9214f4f40827c9eb2e4bc57232023-11-21T21:48:15ZengMDPI AGRemote Sensing2072-42922021-05-011311211810.3390/rs13112118An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV ImagesPyung-chae Lim0Sooahm Rhee1Junghoon Seo2Jae-In Kim3Junhwa Chi4Suk-bae Lee5Taejung Kim6Image Engineering Research Center, 3DLabs Co. Ltd., Incheon 21984, KoreaImage Engineering Research Center, 3DLabs Co. Ltd., Incheon 21984, KoreaDepartment of Geoinformatic Engineering, Inha University, Incheon 22212, KoreaCenter of RS & GIS, Korea Polar Research Institute, Incheon 21990, KoreaCenter of RS & GIS, Korea Polar Research Institute, Incheon 21990, KoreaDepartment of Civil Engineering, Gyeongsangnam National University of Science and Technology, Jinju 52725, KoreaDepartment of Geoinformatic Engineering, Inha University, Incheon 22212, KoreaRecently, the mapping industry has been focusing on the possibility of large-scale mapping from unmanned aerial vehicles (UAVs) owing to advantages such as easy operation and cost reduction. In order to produce large-scale maps from UAV images, it is important to obtain precise orientation parameters as well as analyzing the sharpness of they themselves measured through image analysis. For this, various techniques have been developed and are included in most of the commercial UAV image processing software. For mapping, it is equally important to select images that can cover a region of interest (ROI) with the fewest possible images. Otherwise, to map the ROI, one may have to handle too many images, and commercial software does not provide information needed to select images, nor does it explicitly explain how to select images for mapping. For these reasons, stereo mapping of UAV images in particular is time consuming and costly. In order to solve these problems, this study proposes a method to select images intelligently. We can select a minimum number of image pairs to cover the ROI with the fewest possible images. We can also select optimal image pairs to cover the ROI with the most accurate stereo pairs. We group images by strips and generate the initial image pairs. We then apply an intelligent scheme to iteratively select optimal image pairs from the start to the end of an image strip. According to the results of the experiment, the number of images selected is greatly reduced by applying the proposed optimal image–composition algorithm. The selected image pairs produce a dense 3D point cloud over the ROI without any holes. For stereoscopic plotting, the selected image pairs were map the ROI successfully on a digital photogrammetric workstation (DPW) and a digital map covering the ROI is generated. The proposed method should contribute to time and cost reductions in UAV mapping.https://www.mdpi.com/2072-4292/13/11/2118UAV imagesmonoscopic mappingstereoscopic plottingimage overlapoptimal image selection |
| spellingShingle | Pyung-chae Lim Sooahm Rhee Junghoon Seo Jae-In Kim Junhwa Chi Suk-bae Lee Taejung Kim An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images Remote Sensing UAV images monoscopic mapping stereoscopic plotting image overlap optimal image selection |
| title | An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images |
| title_full | An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images |
| title_fullStr | An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images |
| title_full_unstemmed | An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images |
| title_short | An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images |
| title_sort | optimal image selection algorithm for large scale stereoscopic mapping of uav images |
| topic | UAV images monoscopic mapping stereoscopic plotting image overlap optimal image selection |
| url | https://www.mdpi.com/2072-4292/13/11/2118 |
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