Building extraction method for aerial images based on DeepLabv3+ semantic segmentation
Aerial imagery can provide rich geographic information. As an important ground object information, quickly and accurately extracting buildings from aerial images can achieve target monitoring, location positioning, and further enrich specific geographic information in a given area. To address the is...
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Editorial Office of Command Control and Simulation
2024-12-01
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| Series: | Zhihui kongzhi yu fangzhen |
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| Online Access: | https://www.zhkzyfz.cn/fileup/1673-3819/PDF/1732684200431-2040351917.pdf |
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| _version_ | 1846151706715357184 |
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| author | LIAO Yuanhui, WANG Jingdong, LI Haoran, YANG Heng |
| author_facet | LIAO Yuanhui, WANG Jingdong, LI Haoran, YANG Heng |
| author_sort | LIAO Yuanhui, WANG Jingdong, LI Haoran, YANG Heng |
| collection | DOAJ |
| description | Aerial imagery can provide rich geographic information. As an important ground object information, quickly and accurately extracting buildings from aerial images can achieve target monitoring, location positioning, and further enrich specific geographic information in a given area. To address the issues of segmentation result merging and irregular contour lines in semantic segmentation algorithms for building extraction, an improved model based on DeepLabv3+ for aerial building extraction is proposed by improving the feature fusion structure, constructing a comprehensive loss function, and incorporating an improved Douglas Peucker algorithm. Experimental results show that the improved model achieves an IoU of 0.794 on the test set, a 14.7% improvement compared to the original model. It effectively avoids the problem of merged segmentation between neighboring buildings and results in more regular segmentation boundaries, enabling more accurate extraction of the building contours. |
| format | Article |
| id | doaj-art-a30ad6ca7fbd4cb4aaacd86ddeaf31c9 |
| institution | Kabale University |
| issn | 1673-3819 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Office of Command Control and Simulation |
| record_format | Article |
| series | Zhihui kongzhi yu fangzhen |
| spelling | doaj-art-a30ad6ca7fbd4cb4aaacd86ddeaf31c92024-11-27T05:28:12ZzhoEditorial Office of Command Control and SimulationZhihui kongzhi yu fangzhen1673-38192024-12-01466556110.3969/j.issn.1673-3819.2024.06.010Building extraction method for aerial images based on DeepLabv3+ semantic segmentationLIAO Yuanhui, WANG Jingdong, LI Haoran, YANG Heng0College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaAerial imagery can provide rich geographic information. As an important ground object information, quickly and accurately extracting buildings from aerial images can achieve target monitoring, location positioning, and further enrich specific geographic information in a given area. To address the issues of segmentation result merging and irregular contour lines in semantic segmentation algorithms for building extraction, an improved model based on DeepLabv3+ for aerial building extraction is proposed by improving the feature fusion structure, constructing a comprehensive loss function, and incorporating an improved Douglas Peucker algorithm. Experimental results show that the improved model achieves an IoU of 0.794 on the test set, a 14.7% improvement compared to the original model. It effectively avoids the problem of merged segmentation between neighboring buildings and results in more regular segmentation boundaries, enabling more accurate extraction of the building contours.https://www.zhkzyfz.cn/fileup/1673-3819/PDF/1732684200431-2040351917.pdfaerial image|building extraction|deeplabv3+|contour regularization |
| spellingShingle | LIAO Yuanhui, WANG Jingdong, LI Haoran, YANG Heng Building extraction method for aerial images based on DeepLabv3+ semantic segmentation Zhihui kongzhi yu fangzhen aerial image|building extraction|deeplabv3+|contour regularization |
| title | Building extraction method for aerial images based on DeepLabv3+ semantic segmentation |
| title_full | Building extraction method for aerial images based on DeepLabv3+ semantic segmentation |
| title_fullStr | Building extraction method for aerial images based on DeepLabv3+ semantic segmentation |
| title_full_unstemmed | Building extraction method for aerial images based on DeepLabv3+ semantic segmentation |
| title_short | Building extraction method for aerial images based on DeepLabv3+ semantic segmentation |
| title_sort | building extraction method for aerial images based on deeplabv3 semantic segmentation |
| topic | aerial image|building extraction|deeplabv3+|contour regularization |
| url | https://www.zhkzyfz.cn/fileup/1673-3819/PDF/1732684200431-2040351917.pdf |
| work_keys_str_mv | AT liaoyuanhuiwangjingdonglihaoranyangheng buildingextractionmethodforaerialimagesbasedondeeplabv3semanticsegmentation |