An applicable and automatic method for earth surface water mapping based on multispectral images
Earth’s surface water plays an important role in the global water cycle, environmental processes, and human society, and it is necessary to dynamically capture the distribution and extent of surface water on Earth. However, due to the high complexity of the surface environment of Earth, the current...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0303243421001793 |
| _version_ | 1818232993790033920 |
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| author | Xin Luo Xiaohua Tong Zhongwen Hu |
| author_facet | Xin Luo Xiaohua Tong Zhongwen Hu |
| author_sort | Xin Luo |
| collection | DOAJ |
| description | Earth’s surface water plays an important role in the global water cycle, environmental processes, and human society, and it is necessary to dynamically capture the distribution and extent of surface water on Earth. However, due to the high complexity of the surface environment of Earth, the current surface water mapping methods are limited in applicability and precision. In this study, to explore an automatic and applicable model for surface water mapping, particularly for the regions with highly heterogenous backgrounds, we adopted state-of-the-art deep learning techniques and structured a new model, namely, WatNet, for surface water mapping. Specifically, we combined a state-of-the-art image classification model and a semantic segmentation model into an improved deep learning model. For the fine-scale identification of small water bodies, the combined model was further improved with surface water mapping-tailored design. To learn the surface water features of worldwide regions, a surface water knowledge base that consists of worldwide satellite images was built in this study. The newly structured WatNet model was tested on three highly heterogeneous regions, and as demonstrated by the results, 1) the trained WatNet model achieved the highest accuracies, which were above 95%, for all the selected test regions; 2) the new structured WatNet model yields significant improvements through state-of-the-art model combinations and the surface water-tailored design; and 3) unlike conventional methods, which usually require parameterization in accordance with the specific surface environment, trained WatNet can be directly applied for highly accurate surface water mapping, and, thus, no human labor is required. |
| first_indexed | 2024-12-12T11:15:07Z |
| format | Article |
| id | doaj.art-abc2e8752c6945198001bf2e70979b93 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-12-12T11:15:07Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-abc2e8752c6945198001bf2e70979b932022-12-22T00:26:10ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322021-12-01103102472An applicable and automatic method for earth surface water mapping based on multispectral imagesXin Luo0Xiaohua Tong1Zhongwen Hu2Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China; College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China; Corresponding authors at: Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China.Tongji University, College of Surveying and Geo-informatics, 1239 Siping Road, Shanghai 200092, ChinaKey Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China; Corresponding authors at: Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China.Earth’s surface water plays an important role in the global water cycle, environmental processes, and human society, and it is necessary to dynamically capture the distribution and extent of surface water on Earth. However, due to the high complexity of the surface environment of Earth, the current surface water mapping methods are limited in applicability and precision. In this study, to explore an automatic and applicable model for surface water mapping, particularly for the regions with highly heterogenous backgrounds, we adopted state-of-the-art deep learning techniques and structured a new model, namely, WatNet, for surface water mapping. Specifically, we combined a state-of-the-art image classification model and a semantic segmentation model into an improved deep learning model. For the fine-scale identification of small water bodies, the combined model was further improved with surface water mapping-tailored design. To learn the surface water features of worldwide regions, a surface water knowledge base that consists of worldwide satellite images was built in this study. The newly structured WatNet model was tested on three highly heterogeneous regions, and as demonstrated by the results, 1) the trained WatNet model achieved the highest accuracies, which were above 95%, for all the selected test regions; 2) the new structured WatNet model yields significant improvements through state-of-the-art model combinations and the surface water-tailored design; and 3) unlike conventional methods, which usually require parameterization in accordance with the specific surface environment, trained WatNet can be directly applied for highly accurate surface water mapping, and, thus, no human labor is required.http://www.sciencedirect.com/science/article/pii/S0303243421001793Surface water mappingDeep learningConvolutional neural networkSurface water knowledge baseApplicabilityAutomatic |
| spellingShingle | Xin Luo Xiaohua Tong Zhongwen Hu An applicable and automatic method for earth surface water mapping based on multispectral images International Journal of Applied Earth Observations and Geoinformation Surface water mapping Deep learning Convolutional neural network Surface water knowledge base Applicability Automatic |
| title | An applicable and automatic method for earth surface water mapping based on multispectral images |
| title_full | An applicable and automatic method for earth surface water mapping based on multispectral images |
| title_fullStr | An applicable and automatic method for earth surface water mapping based on multispectral images |
| title_full_unstemmed | An applicable and automatic method for earth surface water mapping based on multispectral images |
| title_short | An applicable and automatic method for earth surface water mapping based on multispectral images |
| title_sort | applicable and automatic method for earth surface water mapping based on multispectral images |
| topic | Surface water mapping Deep learning Convolutional neural network Surface water knowledge base Applicability Automatic |
| url | http://www.sciencedirect.com/science/article/pii/S0303243421001793 |
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