CS-CapsFPN: A Context-Augmentation and Self-Attention Capsule Feature Pyramid Network for Road Network Extraction from Remote Sensing Imagery
The information-accurate road network database is greatly significant and provides essential input to many transportation-related activities. Recently, remote sensing images have been an important data source for assisting rapid road network updating tasks. However, due to the diverse challenging sc...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-05-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2021.1929884 |
| _version_ | 1797661102176206848 |
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| author | Yongtao Yu Jun Wang Haiyan Guan Shenghua Jin Yongjun Zhang Changhui Yu E. Tang Shaozhang Xiao Jonathan Li |
| author_facet | Yongtao Yu Jun Wang Haiyan Guan Shenghua Jin Yongjun Zhang Changhui Yu E. Tang Shaozhang Xiao Jonathan Li |
| author_sort | Yongtao Yu |
| collection | DOAJ |
| description | The information-accurate road network database is greatly significant and provides essential input to many transportation-related activities. Recently, remote sensing images have been an important data source for assisting rapid road network updating tasks. However, due to the diverse challenging scenarios of roads in remote sensing images, such as occlusions, shadows, material diversities, and topology variations, it is still difficult to realize highly accurate extraction of roads. This paper proposes a novel context-augmentation and self-attention capsule feature pyramid network (CS-CapsFPN) to extract roads from remote sensing images. By designing a capsule feature pyramid network architecture, the proposed CS-CapsFPN can extract and fuze different-level and different-scale high-order capsule features to provide a high-resolution and semantically strong feature representation for predicting the road region maps. By integrating the context-augmentation and self-attention modules, the proposed CS-CapsFPN can exploit multi-scale contextual properties at a high-resolution perspective and emphasize channel-wise informative features to further enhance the feature representation robustness. Quantitative evaluations on two test datasets show that the proposed CS-CapsFPN achieves a competitive performance with a precision, recall, intersection-over-union, and Fscore of 0.9470, 0.9407, 0.8957, and 0.9438, respectively. Comparative studies also confirm the feasibility and superiority of the proposed CS-CapsFPN in road extraction tasks. |
| first_indexed | 2024-03-11T18:40:26Z |
| format | Article |
| id | doaj.art-253979a25f164c09a556031a53f52e0d |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:40:26Z |
| publishDate | 2021-05-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-253979a25f164c09a556031a53f52e0d2023-10-12T13:36:24ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712021-05-0147349951710.1080/07038992.2021.19298841929884CS-CapsFPN: A Context-Augmentation and Self-Attention Capsule Feature Pyramid Network for Road Network Extraction from Remote Sensing ImageryYongtao Yu0Jun Wang1Haiyan Guan2Shenghua Jin3Yongjun Zhang4Changhui Yu5E. Tang6Shaozhang Xiao7Jonathan Li8Faculty of Computer and Software Engineering, Huaiyin Institute of TechnologyFaculty of Computer and Software Engineering, Huaiyin Institute of TechnologySchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and TechnologyFaculty of Computer and Software Engineering, Huaiyin Institute of TechnologyFaculty of Computer and Software Engineering, Huaiyin Institute of TechnologyFaculty of Computer and Software Engineering, Huaiyin Institute of TechnologyFaculty of Computer and Software Engineering, Huaiyin Institute of TechnologyFaculty of Computer and Software Engineering, Huaiyin Institute of TechnologyDepartment of Geography and Environmental Management, University of WaterlooThe information-accurate road network database is greatly significant and provides essential input to many transportation-related activities. Recently, remote sensing images have been an important data source for assisting rapid road network updating tasks. However, due to the diverse challenging scenarios of roads in remote sensing images, such as occlusions, shadows, material diversities, and topology variations, it is still difficult to realize highly accurate extraction of roads. This paper proposes a novel context-augmentation and self-attention capsule feature pyramid network (CS-CapsFPN) to extract roads from remote sensing images. By designing a capsule feature pyramid network architecture, the proposed CS-CapsFPN can extract and fuze different-level and different-scale high-order capsule features to provide a high-resolution and semantically strong feature representation for predicting the road region maps. By integrating the context-augmentation and self-attention modules, the proposed CS-CapsFPN can exploit multi-scale contextual properties at a high-resolution perspective and emphasize channel-wise informative features to further enhance the feature representation robustness. Quantitative evaluations on two test datasets show that the proposed CS-CapsFPN achieves a competitive performance with a precision, recall, intersection-over-union, and Fscore of 0.9470, 0.9407, 0.8957, and 0.9438, respectively. Comparative studies also confirm the feasibility and superiority of the proposed CS-CapsFPN in road extraction tasks.http://dx.doi.org/10.1080/07038992.2021.1929884 |
| spellingShingle | Yongtao Yu Jun Wang Haiyan Guan Shenghua Jin Yongjun Zhang Changhui Yu E. Tang Shaozhang Xiao Jonathan Li CS-CapsFPN: A Context-Augmentation and Self-Attention Capsule Feature Pyramid Network for Road Network Extraction from Remote Sensing Imagery Canadian Journal of Remote Sensing |
| title | CS-CapsFPN: A Context-Augmentation and Self-Attention Capsule Feature Pyramid Network for Road Network Extraction from Remote Sensing Imagery |
| title_full | CS-CapsFPN: A Context-Augmentation and Self-Attention Capsule Feature Pyramid Network for Road Network Extraction from Remote Sensing Imagery |
| title_fullStr | CS-CapsFPN: A Context-Augmentation and Self-Attention Capsule Feature Pyramid Network for Road Network Extraction from Remote Sensing Imagery |
| title_full_unstemmed | CS-CapsFPN: A Context-Augmentation and Self-Attention Capsule Feature Pyramid Network for Road Network Extraction from Remote Sensing Imagery |
| title_short | CS-CapsFPN: A Context-Augmentation and Self-Attention Capsule Feature Pyramid Network for Road Network Extraction from Remote Sensing Imagery |
| title_sort | cs capsfpn a context augmentation and self attention capsule feature pyramid network for road network extraction from remote sensing imagery |
| url | http://dx.doi.org/10.1080/07038992.2021.1929884 |
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