AP-PointRend: An Improved Network for Building Extraction via High-Resolution Remote Sensing Images
The automatic extraction of buildings from remote sensing images is crucial for various applications such as urban planning and management, emergency response, and map making and updating. In recent years, deep learning (DL) methods have made significant progress in this field. However, due to the c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/9/1481 |
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| author | Bowen Zhu Ding Yu Xiongwu Xiao Jian Shen Zhigao Cui Yanzhao Su Aihua Li Deren Li |
| author_facet | Bowen Zhu Ding Yu Xiongwu Xiao Jian Shen Zhigao Cui Yanzhao Su Aihua Li Deren Li |
| author_sort | Bowen Zhu |
| collection | DOAJ |
| description | The automatic extraction of buildings from remote sensing images is crucial for various applications such as urban planning and management, emergency response, and map making and updating. In recent years, deep learning (DL) methods have made significant progress in this field. However, due to the complex and diverse structures of buildings and their interconnections, the accuracy of extracted buildings remains insufficient for high-precision applications such as maps and navigation. To address the issue of enhancing building boundary extraction, we propose a modified instance segmentation model, AP-PointRend (Adaptive Parameter-PointRend), to improve the performance of building instance extraction. Specifically, the model can adaptively select the number of iterations and points based on the size of buildings to improve the segmentation accuracy of large buildings. By introducing regularization constraints, discrete small patches are removed, preserving boundaries better during the segmentation process. We also designed an image merging method to eliminate seams, ensure the recall rate, and improve the extraction accuracy. The Vaihingen and WHU benchmark datasets were used to evaluate the performance of the AP-PointRend method. The experimental results showed that the proposed AP-PointRend approach generated better building extraction results compared with other state-of-the-art methods. |
| format | Article |
| id | doaj-art-a9015b2da6df4c0c994c1e4f9109fa79 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-a9015b2da6df4c0c994c1e4f9109fa792025-08-20T02:59:08ZengMDPI AGRemote Sensing2072-42922025-04-01179148110.3390/rs17091481AP-PointRend: An Improved Network for Building Extraction via High-Resolution Remote Sensing ImagesBowen Zhu0Ding Yu1Xiongwu Xiao2Jian Shen3Zhigao Cui4Yanzhao Su5Aihua Li6Deren Li7School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, ChinaKey Laboratory of the Ministry of Education on Application of Artificial Intelligence in Equipment, Xi’an Research Institute of High Technology, Xi’an 710025, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, ChinaSchool of Computer Science, Wuhan University, Wuhan 430072, ChinaKey Laboratory of the Ministry of Education on Application of Artificial Intelligence in Equipment, Xi’an Research Institute of High Technology, Xi’an 710025, ChinaKey Laboratory of the Ministry of Education on Application of Artificial Intelligence in Equipment, Xi’an Research Institute of High Technology, Xi’an 710025, ChinaKey Laboratory of the Ministry of Education on Application of Artificial Intelligence in Equipment, Xi’an Research Institute of High Technology, Xi’an 710025, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, ChinaThe automatic extraction of buildings from remote sensing images is crucial for various applications such as urban planning and management, emergency response, and map making and updating. In recent years, deep learning (DL) methods have made significant progress in this field. However, due to the complex and diverse structures of buildings and their interconnections, the accuracy of extracted buildings remains insufficient for high-precision applications such as maps and navigation. To address the issue of enhancing building boundary extraction, we propose a modified instance segmentation model, AP-PointRend (Adaptive Parameter-PointRend), to improve the performance of building instance extraction. Specifically, the model can adaptively select the number of iterations and points based on the size of buildings to improve the segmentation accuracy of large buildings. By introducing regularization constraints, discrete small patches are removed, preserving boundaries better during the segmentation process. We also designed an image merging method to eliminate seams, ensure the recall rate, and improve the extraction accuracy. The Vaihingen and WHU benchmark datasets were used to evaluate the performance of the AP-PointRend method. The experimental results showed that the proposed AP-PointRend approach generated better building extraction results compared with other state-of-the-art methods.https://www.mdpi.com/2072-4292/17/9/1481building extractioninstance segmentationdeep learningremote sensing |
| spellingShingle | Bowen Zhu Ding Yu Xiongwu Xiao Jian Shen Zhigao Cui Yanzhao Su Aihua Li Deren Li AP-PointRend: An Improved Network for Building Extraction via High-Resolution Remote Sensing Images Remote Sensing building extraction instance segmentation deep learning remote sensing |
| title | AP-PointRend: An Improved Network for Building Extraction via High-Resolution Remote Sensing Images |
| title_full | AP-PointRend: An Improved Network for Building Extraction via High-Resolution Remote Sensing Images |
| title_fullStr | AP-PointRend: An Improved Network for Building Extraction via High-Resolution Remote Sensing Images |
| title_full_unstemmed | AP-PointRend: An Improved Network for Building Extraction via High-Resolution Remote Sensing Images |
| title_short | AP-PointRend: An Improved Network for Building Extraction via High-Resolution Remote Sensing Images |
| title_sort | ap pointrend an improved network for building extraction via high resolution remote sensing images |
| topic | building extraction instance segmentation deep learning remote sensing |
| url | https://www.mdpi.com/2072-4292/17/9/1481 |
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