HSRoadNet: Hard-Swish Activation Function and Improved Squeeze–Excitation Module Network for Road Extraction Using Satellite Remote Sensing Imagery
Road information plays an essential role in many fields. To prevent failed extraction of heterogeneous regions, fracture of extracted roads and others resulted from vehicles and trees when using very high resolution remote sensing images; a remote sensing image road extraction method based on Hard-S...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10850767/ |
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| author | Xunqiang Gong Yingjie Ma Ailong Ma Zhaoyang Hou Meng Zhang Yanfei Zhong |
| author_facet | Xunqiang Gong Yingjie Ma Ailong Ma Zhaoyang Hou Meng Zhang Yanfei Zhong |
| author_sort | Xunqiang Gong |
| collection | DOAJ |
| description | Road information plays an essential role in many fields. To prevent failed extraction of heterogeneous regions, fracture of extracted roads and others resulted from vehicles and trees when using very high resolution remote sensing images; a remote sensing image road extraction method based on Hard-Swish Squeeze–Excitation RoadNet is proposed in this article. First, road extraction task is divided into three correlated subtasks to reduce the impact of vehicles and trees in road extracting. Second, a normalization layer is adopted to prevent gradient levels from vanishing and exploring and avoid fracture of the extracted road. Then, adopting Hard-Swish activation function to improve the accuracy of road extracting, and then finally, using the improved squeeze–excitation module to make the trained net a full use of the characteristic information of the road that do not increase excessive capacity. Comparison experimental results indicate that, in various indicators, the proposed method performs serviceably, it, respectively, increased by 16.8%, 2.2%, 1.5%, and 8.5% over the suboptimal in <italic>F</italic>-score, global accuracy, class average accuracy, and recall ratio. The mean intersection over union (MIoU) value of the proposed method was the suboptimum with a disparity of 0.2% from the optimal. Ablation experiments show that the proposed method performs best in various indices, and the global accuracy, MIoU, class average accuracy, and recall rate are improved by 0.5%, 0.1%, 0.5%, and 0.2%, respectively, compared with the suboptimal method. The <italic>F</italic>-score is suboptimal, with a 0.3% difference from the best. |
| format | Article |
| id | doaj-art-cac276d07faa4671970865f266b5940e |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-cac276d07faa4671970865f266b5940e2025-02-12T00:00:59ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184907492010.1109/JSTARS.2025.353319610850767HSRoadNet: Hard-Swish Activation Function and Improved Squeeze–Excitation Module Network for Road Extraction Using Satellite Remote Sensing ImageryXunqiang Gong0https://orcid.org/0000-0002-6700-2241Yingjie Ma1https://orcid.org/0009-0004-1694-3932Ailong Ma2https://orcid.org/0000-0003-3692-6473Zhaoyang Hou3https://orcid.org/0000-0001-9894-4795Meng Zhang4Yanfei Zhong5https://orcid.org/0000-0001-9446-5850Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, East China University of Technology, Nanchang, ChinaKey Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, East China University of Technology, Nanchang, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, ChinaKey Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, East China University of Technology, Nanchang, ChinaJiangxi Academy of Eco-Environmental Sciences and Planning, Nanchang, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, ChinaRoad information plays an essential role in many fields. To prevent failed extraction of heterogeneous regions, fracture of extracted roads and others resulted from vehicles and trees when using very high resolution remote sensing images; a remote sensing image road extraction method based on Hard-Swish Squeeze–Excitation RoadNet is proposed in this article. First, road extraction task is divided into three correlated subtasks to reduce the impact of vehicles and trees in road extracting. Second, a normalization layer is adopted to prevent gradient levels from vanishing and exploring and avoid fracture of the extracted road. Then, adopting Hard-Swish activation function to improve the accuracy of road extracting, and then finally, using the improved squeeze–excitation module to make the trained net a full use of the characteristic information of the road that do not increase excessive capacity. Comparison experimental results indicate that, in various indicators, the proposed method performs serviceably, it, respectively, increased by 16.8%, 2.2%, 1.5%, and 8.5% over the suboptimal in <italic>F</italic>-score, global accuracy, class average accuracy, and recall ratio. The mean intersection over union (MIoU) value of the proposed method was the suboptimum with a disparity of 0.2% from the optimal. Ablation experiments show that the proposed method performs best in various indices, and the global accuracy, MIoU, class average accuracy, and recall rate are improved by 0.5%, 0.1%, 0.5%, and 0.2%, respectively, compared with the suboptimal method. The <italic>F</italic>-score is suboptimal, with a 0.3% difference from the best.https://ieeexplore.ieee.org/document/10850767/Activation functionimproved squeeze-excitation (SE) modulenormalization layerroad extraction of remote sensing imageRoadNet |
| spellingShingle | Xunqiang Gong Yingjie Ma Ailong Ma Zhaoyang Hou Meng Zhang Yanfei Zhong HSRoadNet: Hard-Swish Activation Function and Improved Squeeze–Excitation Module Network for Road Extraction Using Satellite Remote Sensing Imagery IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Activation function improved squeeze-excitation (SE) module normalization layer road extraction of remote sensing image RoadNet |
| title | HSRoadNet: Hard-Swish Activation Function and Improved Squeeze–Excitation Module Network for Road Extraction Using Satellite Remote Sensing Imagery |
| title_full | HSRoadNet: Hard-Swish Activation Function and Improved Squeeze–Excitation Module Network for Road Extraction Using Satellite Remote Sensing Imagery |
| title_fullStr | HSRoadNet: Hard-Swish Activation Function and Improved Squeeze–Excitation Module Network for Road Extraction Using Satellite Remote Sensing Imagery |
| title_full_unstemmed | HSRoadNet: Hard-Swish Activation Function and Improved Squeeze–Excitation Module Network for Road Extraction Using Satellite Remote Sensing Imagery |
| title_short | HSRoadNet: Hard-Swish Activation Function and Improved Squeeze–Excitation Module Network for Road Extraction Using Satellite Remote Sensing Imagery |
| title_sort | hsroadnet hard swish activation function and improved squeeze x2013 excitation module network for road extraction using satellite remote sensing imagery |
| topic | Activation function improved squeeze-excitation (SE) module normalization layer road extraction of remote sensing image RoadNet |
| url | https://ieeexplore.ieee.org/document/10850767/ |
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