Consistency Self-Training Semi-Supervised Method for Road Extraction from Remote Sensing Images
Road extraction techniques based on remote sensing image have significantly advanced. Currently, fully supervised road segmentation neural networks based on remote sensing images require a significant number of densely labeled road samples, limiting their applicability in large-scale scenarios. Cons...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/21/3945 |
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| _version_ | 1850062336723779584 |
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| author | Xingjian Gu Supeng Yu Fen Huang Shougang Ren Chengcheng Fan |
| author_facet | Xingjian Gu Supeng Yu Fen Huang Shougang Ren Chengcheng Fan |
| author_sort | Xingjian Gu |
| collection | DOAJ |
| description | Road extraction techniques based on remote sensing image have significantly advanced. Currently, fully supervised road segmentation neural networks based on remote sensing images require a significant number of densely labeled road samples, limiting their applicability in large-scale scenarios. Consequently, semi-supervised methods that utilize fewer labeled data have gained increasing attention. However, the imbalance between a small quantity of labeled data and a large volume of unlabeled data leads to local detail errors and overall cognitive mistakes in semi-supervised road extraction. To address this challenge, this paper proposes a novel consistency self-training semi-supervised method (CSSnet), which effectively learns from a limited number of labeled data samples and a large amount of unlabeled data. This method integrates self-training semi-supervised segmentation with semi-supervised classification. The semi-supervised segmentation component relies on an enhanced generative adversarial network for semantic segmentation, which significantly reduces local detail errors. The semi-supervised classification component relies on an upgraded mean-teacher network to handle overall cognitive errors. Our method exhibits excellent performance with a modest amount of labeled data. This study was validated on three separate road datasets comprising high-resolution remote sensing satellite images and UAV photographs. Experimental findings showed that our method consistently outperformed state-of-the-art semi-supervised methods and several classic fully supervised methods. |
| format | Article |
| id | doaj-art-ffd07efe1e404cfeaa9a04a97a5a0022 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-ffd07efe1e404cfeaa9a04a97a5a00222025-08-20T02:49:56ZengMDPI AGRemote Sensing2072-42922024-10-011621394510.3390/rs16213945Consistency Self-Training Semi-Supervised Method for Road Extraction from Remote Sensing ImagesXingjian Gu0Supeng Yu1Fen Huang2Shougang Ren3Chengcheng Fan4College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, ChinaInnovation Academy for Microsatellites of CAS, Shanghai 201210, ChinaRoad extraction techniques based on remote sensing image have significantly advanced. Currently, fully supervised road segmentation neural networks based on remote sensing images require a significant number of densely labeled road samples, limiting their applicability in large-scale scenarios. Consequently, semi-supervised methods that utilize fewer labeled data have gained increasing attention. However, the imbalance between a small quantity of labeled data and a large volume of unlabeled data leads to local detail errors and overall cognitive mistakes in semi-supervised road extraction. To address this challenge, this paper proposes a novel consistency self-training semi-supervised method (CSSnet), which effectively learns from a limited number of labeled data samples and a large amount of unlabeled data. This method integrates self-training semi-supervised segmentation with semi-supervised classification. The semi-supervised segmentation component relies on an enhanced generative adversarial network for semantic segmentation, which significantly reduces local detail errors. The semi-supervised classification component relies on an upgraded mean-teacher network to handle overall cognitive errors. Our method exhibits excellent performance with a modest amount of labeled data. This study was validated on three separate road datasets comprising high-resolution remote sensing satellite images and UAV photographs. Experimental findings showed that our method consistently outperformed state-of-the-art semi-supervised methods and several classic fully supervised methods.https://www.mdpi.com/2072-4292/16/21/3945semi-supervisedsemantic segmentationgenerative adversarial networkroad extractionremote sensing image |
| spellingShingle | Xingjian Gu Supeng Yu Fen Huang Shougang Ren Chengcheng Fan Consistency Self-Training Semi-Supervised Method for Road Extraction from Remote Sensing Images Remote Sensing semi-supervised semantic segmentation generative adversarial network road extraction remote sensing image |
| title | Consistency Self-Training Semi-Supervised Method for Road Extraction from Remote Sensing Images |
| title_full | Consistency Self-Training Semi-Supervised Method for Road Extraction from Remote Sensing Images |
| title_fullStr | Consistency Self-Training Semi-Supervised Method for Road Extraction from Remote Sensing Images |
| title_full_unstemmed | Consistency Self-Training Semi-Supervised Method for Road Extraction from Remote Sensing Images |
| title_short | Consistency Self-Training Semi-Supervised Method for Road Extraction from Remote Sensing Images |
| title_sort | consistency self training semi supervised method for road extraction from remote sensing images |
| topic | semi-supervised semantic segmentation generative adversarial network road extraction remote sensing image |
| url | https://www.mdpi.com/2072-4292/16/21/3945 |
| work_keys_str_mv | AT xingjiangu consistencyselftrainingsemisupervisedmethodforroadextractionfromremotesensingimages AT supengyu consistencyselftrainingsemisupervisedmethodforroadextractionfromremotesensingimages AT fenhuang consistencyselftrainingsemisupervisedmethodforroadextractionfromremotesensingimages AT shougangren consistencyselftrainingsemisupervisedmethodforroadextractionfromremotesensingimages AT chengchengfan consistencyselftrainingsemisupervisedmethodforroadextractionfromremotesensingimages |