Urban Surface Water Mapping from VHR Images Based on Superpixel Segmentation and Target Detection
Surface water is a fundamental resource in urban environments. Monitoring the spatio-temporal distribution of urban surface water from remotely sensed images is crucial for urban planning and management. Unfortunately, due to the limitation of spatial resolution, the method based on low/m...
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IEEE
2022-01-01
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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/9794451/ |
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| author | Qingwei Liu Yugang Tian Lihao Zhang Bo Chen |
| author_facet | Qingwei Liu Yugang Tian Lihao Zhang Bo Chen |
| author_sort | Qingwei Liu |
| collection | DOAJ |
| description | Surface water is a fundamental resource in urban environments. Monitoring the spatio-temporal distribution of urban surface water from remotely sensed images is crucial for urban planning and management. Unfortunately, due to the limitation of spatial resolution, the method based on low/medium resolution images is difficult to extract small water bodies accurately. Recently, very high resolution (VHR) images have shown considerable potential for urban compositions mapping. However, fewer spectral bands, shadows, and high spectral heterogeneity of VHR images hinder the application of traditional methods. In this article, we proposed an urban surface water mapping method called sparse superpixel-based water extraction (SSWE) from VHR images. The method includes three steps: clustering water bodies into sparse targets at the object level by an improved scale-adaptive simple non-iterative clustering (SA-SNIC) superpixel segmentation; generating new bands with additional spectral, spatial, and derived features, to increase the dimensions of original data and enhance the separability between water bodies and background covers; and constructing a positive-negative constrained energy minimization multitarget sparse detector to highlight the water bodies while suppressing shadows. The proposed method was applied to GF-2 multispectral images of four cities in China. The results showed that SSWE achieved the highest accuracy compared with other methods, with an average OA of 98.91% and an average kappa coefficient of 0.942. Furthermore, the separability analysis also indicated that SSWE could effectively distinguish urban water bodies from shadows and other land covers. Stable results can be acquired by the suggested parameters and thresholds of SSWE. |
| format | Article |
| id | doaj-art-886d283d45f64c6198ef6825fc930e4d |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-886d283d45f64c6198ef6825fc930e4d2025-02-05T00:00:08ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352022-01-01155339535610.1109/JSTARS.2022.31817209794451Urban Surface Water Mapping from VHR Images Based on Superpixel Segmentation and Target DetectionQingwei Liu0Yugang Tian1https://orcid.org/0000-0002-0455-2094Lihao Zhang2Bo Chen3https://orcid.org/0000-0001-8883-8358School of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing, ChinaSurface water is a fundamental resource in urban environments. Monitoring the spatio-temporal distribution of urban surface water from remotely sensed images is crucial for urban planning and management. Unfortunately, due to the limitation of spatial resolution, the method based on low/medium resolution images is difficult to extract small water bodies accurately. Recently, very high resolution (VHR) images have shown considerable potential for urban compositions mapping. However, fewer spectral bands, shadows, and high spectral heterogeneity of VHR images hinder the application of traditional methods. In this article, we proposed an urban surface water mapping method called sparse superpixel-based water extraction (SSWE) from VHR images. The method includes three steps: clustering water bodies into sparse targets at the object level by an improved scale-adaptive simple non-iterative clustering (SA-SNIC) superpixel segmentation; generating new bands with additional spectral, spatial, and derived features, to increase the dimensions of original data and enhance the separability between water bodies and background covers; and constructing a positive-negative constrained energy minimization multitarget sparse detector to highlight the water bodies while suppressing shadows. The proposed method was applied to GF-2 multispectral images of four cities in China. The results showed that SSWE achieved the highest accuracy compared with other methods, with an average OA of 98.91% and an average kappa coefficient of 0.942. Furthermore, the separability analysis also indicated that SSWE could effectively distinguish urban water bodies from shadows and other land covers. Stable results can be acquired by the suggested parameters and thresholds of SSWE.https://ieeexplore.ieee.org/document/9794451/Sparse target detectionsuperpixel segmentationurban surface water mappingvery high resolution (VHR) images |
| spellingShingle | Qingwei Liu Yugang Tian Lihao Zhang Bo Chen Urban Surface Water Mapping from VHR Images Based on Superpixel Segmentation and Target Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Sparse target detection superpixel segmentation urban surface water mapping very high resolution (VHR) images |
| title | Urban Surface Water Mapping from VHR Images Based on Superpixel Segmentation and Target Detection |
| title_full | Urban Surface Water Mapping from VHR Images Based on Superpixel Segmentation and Target Detection |
| title_fullStr | Urban Surface Water Mapping from VHR Images Based on Superpixel Segmentation and Target Detection |
| title_full_unstemmed | Urban Surface Water Mapping from VHR Images Based on Superpixel Segmentation and Target Detection |
| title_short | Urban Surface Water Mapping from VHR Images Based on Superpixel Segmentation and Target Detection |
| title_sort | urban surface water mapping from vhr images based on superpixel segmentation and target detection |
| topic | Sparse target detection superpixel segmentation urban surface water mapping very high resolution (VHR) images |
| url | https://ieeexplore.ieee.org/document/9794451/ |
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