A novel method integrating sample migration and threshold optimization for high-precision greenhouse classification: evidence from Southern China
Greenhouses are vital for food security and agricultural modernization, yet their classification in remote sensing imagery is challenging due to scattered distribution, small scale, and spectral similarities. This study proposes a remote sensing classification framework using sample migration and dy...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2527308 |
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| _version_ | 1849470237678764032 |
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| author | Kaiyue Luo Henggang Zhang Chenhui Zhu Tianyu Jiao Alim Samat Yonglin Chen Chuanxiang Cheng |
| author_facet | Kaiyue Luo Henggang Zhang Chenhui Zhu Tianyu Jiao Alim Samat Yonglin Chen Chuanxiang Cheng |
| author_sort | Kaiyue Luo |
| collection | DOAJ |
| description | Greenhouses are vital for food security and agricultural modernization, yet their classification in remote sensing imagery is challenging due to scattered distribution, small scale, and spectral similarities. This study proposes a remote sensing classification framework using sample migration and dynamic threshold optimization for accurate, scalable greenhouse detection. Historical samples are migrated to the target area based on spectral similarity, reducing reliance on new labeled datasets and improving cross-regional generalization. A post-classification threshold optimization module corrects misclassifications using multi-dimensional indices, enhancing robustness in complex spectral environments. Empirical validation across six provinces in southern China showed superior performance (96.48% OA, 94.36% Kappa), outperforming traditional methods. This framework enables precise mapping of small-scale greenhouses in heterogeneous regions, supporting agricultural monitoring. It aids government agencies in crop area estimation, land-use tracking, and precision agriculture, while private enterprises benefit for asset evaluation and planning, promoting sustainable resource management and addressing large-scale land cover classification challenges. |
| format | Article |
| id | doaj-art-8705ff8218344c7bb44e739ecf4f1f32 |
| institution | Kabale University |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-8705ff8218344c7bb44e739ecf4f1f322025-08-20T03:25:12ZengTaylor & Francis GroupGeocarto International1010-60491752-07622025-12-0140110.1080/10106049.2025.2527308A novel method integrating sample migration and threshold optimization for high-precision greenhouse classification: evidence from Southern ChinaKaiyue Luo0Henggang Zhang1Chenhui Zhu2Tianyu Jiao3Alim Samat4Yonglin Chen5Chuanxiang Cheng6College of Geography and Environmental Engineering, Gannan Normal University, Ganzhou, ChinaCollege of Geography and Environmental Engineering, Gannan Normal University, Ganzhou, ChinaCollege of Geography and Remote Sensing Science, Xinjiang University, Urumqi, ChinaCollege of Geography and Remote Sensing Science, Xinjiang University, Urumqi, ChinaState Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, ChinaCollege of Geography and Environmental Engineering, Gannan Normal University, Ganzhou, ChinaSchool of Geospatial Information, Information Engineering University, Zhengzhou, ChinaGreenhouses are vital for food security and agricultural modernization, yet their classification in remote sensing imagery is challenging due to scattered distribution, small scale, and spectral similarities. This study proposes a remote sensing classification framework using sample migration and dynamic threshold optimization for accurate, scalable greenhouse detection. Historical samples are migrated to the target area based on spectral similarity, reducing reliance on new labeled datasets and improving cross-regional generalization. A post-classification threshold optimization module corrects misclassifications using multi-dimensional indices, enhancing robustness in complex spectral environments. Empirical validation across six provinces in southern China showed superior performance (96.48% OA, 94.36% Kappa), outperforming traditional methods. This framework enables precise mapping of small-scale greenhouses in heterogeneous regions, supporting agricultural monitoring. It aids government agencies in crop area estimation, land-use tracking, and precision agriculture, while private enterprises benefit for asset evaluation and planning, promoting sustainable resource management and addressing large-scale land cover classification challenges.https://www.tandfonline.com/doi/10.1080/10106049.2025.2527308Greenhousessample migrationthreshold optimizationremote sensing classificationagricultural monitoring |
| spellingShingle | Kaiyue Luo Henggang Zhang Chenhui Zhu Tianyu Jiao Alim Samat Yonglin Chen Chuanxiang Cheng A novel method integrating sample migration and threshold optimization for high-precision greenhouse classification: evidence from Southern China Geocarto International Greenhouses sample migration threshold optimization remote sensing classification agricultural monitoring |
| title | A novel method integrating sample migration and threshold optimization for high-precision greenhouse classification: evidence from Southern China |
| title_full | A novel method integrating sample migration and threshold optimization for high-precision greenhouse classification: evidence from Southern China |
| title_fullStr | A novel method integrating sample migration and threshold optimization for high-precision greenhouse classification: evidence from Southern China |
| title_full_unstemmed | A novel method integrating sample migration and threshold optimization for high-precision greenhouse classification: evidence from Southern China |
| title_short | A novel method integrating sample migration and threshold optimization for high-precision greenhouse classification: evidence from Southern China |
| title_sort | novel method integrating sample migration and threshold optimization for high precision greenhouse classification evidence from southern china |
| topic | Greenhouses sample migration threshold optimization remote sensing classification agricultural monitoring |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2025.2527308 |
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