Evaluating the performance of pixel-based and object-based multidimensional clustering algorithms for automated surface water mapping
Remote sensing observations of surface water are vital for effective water resource management and sustainable development. Unsupervised classification holds promise for automating large-scale surface water detection, and it helps solve the difficult problem of sample collection in supervised classi...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-07-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2025.2523993 |
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| author | Bohao Li Kai Liu Ming Wang Yanfang Wang Linmei Zhuang Weihua Zhu Chenxia Li Linhao Zhang Yanan Chen |
| author_facet | Bohao Li Kai Liu Ming Wang Yanfang Wang Linmei Zhuang Weihua Zhu Chenxia Li Linhao Zhang Yanan Chen |
| author_sort | Bohao Li |
| collection | DOAJ |
| description | Remote sensing observations of surface water are vital for effective water resource management and sustainable development. Unsupervised classification holds promise for automating large-scale surface water detection, and it helps solve the difficult problem of sample collection in supervised classification. Here, we refined the water identification rule for automated surface water extraction on the basis of multidimensional clustering and a supervised classifier. We subsequently comprehensively investigated the classification performance of k-means, hierarchical clustering, and spectral clustering with 57 different feature combinations (features consisting of two bands, B8 and B12, along with four water indices: the automated water extraction index (AWEI), multiband water index (MBWI), normalized difference water index (NDWI), and modified normalized difference water index (MNDWI)) in eight challenging scenarios in China. These comparative experiments were performed from both pixel-based and object-based perspectives. The results show that pixel-based hierarchical clustering, which uses the optimal feature combination of B8, the NDWI, and the MBWI, is the algorithm with the best overall performance, with kappa coefficients exceeding 0.9 in each scene. The object-based hierarchical clustering using the optimal feature combination B8, B12, MNDWI, and MBWI achieves a kappa coefficient exceeding 0.85 in almost all scenes. This algorithm is suitable for scenes without small water bodies. In addition, the improved surface water identification rule increases the applicability of the automated surface water extraction method, which is based on multidimensional clustering in scenes with snow cover. Finally, we compared the performance of multidimensional clustering algorithms with that of the modified Otsu thresholding method and the multi-index threshold-based algorithm and found that the former has an advantage in automated surface water extraction. |
| format | Article |
| id | doaj-art-60c406bec3284cef90fe664c09bf583e |
| institution | Kabale University |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-60c406bec3284cef90fe664c09bf583e2025-08-20T03:30:33ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-07-0112110.1080/10095020.2025.2523993Evaluating the performance of pixel-based and object-based multidimensional clustering algorithms for automated surface water mappingBohao Li0Kai Liu1Ming Wang2Yanfang Wang3Linmei Zhuang4Weihua Zhu5Chenxia Li6Linhao Zhang7Yanan Chen8Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai, ChinaJoint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai, ChinaJoint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai, ChinaHebei International Joint Research Centre for Remote Sensing of Agricultural Drought Monitoring, Hebei GEO University, Shijiazhuang, ChinaJoint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai, ChinaTransport Planning and Research Institute, Ministry of Transport, Beijing, ChinaCollege of Resources, Environment and Tourism, Capital Normal University, Beijing, ChinaFaculty of Geographical Science, Beijing Normal University, Beijing, ChinaHebei Remote Sensing Centre, Hebei Hydrological Engineering Geological Survey Institute, Shijiazhuang, ChinaRemote sensing observations of surface water are vital for effective water resource management and sustainable development. Unsupervised classification holds promise for automating large-scale surface water detection, and it helps solve the difficult problem of sample collection in supervised classification. Here, we refined the water identification rule for automated surface water extraction on the basis of multidimensional clustering and a supervised classifier. We subsequently comprehensively investigated the classification performance of k-means, hierarchical clustering, and spectral clustering with 57 different feature combinations (features consisting of two bands, B8 and B12, along with four water indices: the automated water extraction index (AWEI), multiband water index (MBWI), normalized difference water index (NDWI), and modified normalized difference water index (MNDWI)) in eight challenging scenarios in China. These comparative experiments were performed from both pixel-based and object-based perspectives. The results show that pixel-based hierarchical clustering, which uses the optimal feature combination of B8, the NDWI, and the MBWI, is the algorithm with the best overall performance, with kappa coefficients exceeding 0.9 in each scene. The object-based hierarchical clustering using the optimal feature combination B8, B12, MNDWI, and MBWI achieves a kappa coefficient exceeding 0.85 in almost all scenes. This algorithm is suitable for scenes without small water bodies. In addition, the improved surface water identification rule increases the applicability of the automated surface water extraction method, which is based on multidimensional clustering in scenes with snow cover. Finally, we compared the performance of multidimensional clustering algorithms with that of the modified Otsu thresholding method and the multi-index threshold-based algorithm and found that the former has an advantage in automated surface water extraction.https://www.tandfonline.com/doi/10.1080/10095020.2025.2523993Water mappingSentinel-2unsupervised classificationmachine learningrandom forestobject-based classification |
| spellingShingle | Bohao Li Kai Liu Ming Wang Yanfang Wang Linmei Zhuang Weihua Zhu Chenxia Li Linhao Zhang Yanan Chen Evaluating the performance of pixel-based and object-based multidimensional clustering algorithms for automated surface water mapping Geo-spatial Information Science Water mapping Sentinel-2 unsupervised classification machine learning random forest object-based classification |
| title | Evaluating the performance of pixel-based and object-based multidimensional clustering algorithms for automated surface water mapping |
| title_full | Evaluating the performance of pixel-based and object-based multidimensional clustering algorithms for automated surface water mapping |
| title_fullStr | Evaluating the performance of pixel-based and object-based multidimensional clustering algorithms for automated surface water mapping |
| title_full_unstemmed | Evaluating the performance of pixel-based and object-based multidimensional clustering algorithms for automated surface water mapping |
| title_short | Evaluating the performance of pixel-based and object-based multidimensional clustering algorithms for automated surface water mapping |
| title_sort | evaluating the performance of pixel based and object based multidimensional clustering algorithms for automated surface water mapping |
| topic | Water mapping Sentinel-2 unsupervised classification machine learning random forest object-based classification |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2025.2523993 |
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