ER-GMMD: Cross-Scene Remote Sensing Classification Method of <italic>Tamarix chinensis</italic> in the Yellow River Estuary
<italic>Tamarix chinensis</italic> effectively prevents coastal erosion, stabilizes the surface of coastal wetlands, and improves the soil quality of saline-alkali land, playing a crucial role in coastal wetland ecosystem restoration. <italic>Tamarix chinensis</italic> exhibi...
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| Format: | Article |
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IEEE
2025-01-01
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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/10829769/ |
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| author | Liying Zhu Yabin Hu Guangbo Ren Na Qiao Ziyue Meng Jianbu Wang Yajie Zhao Shibao Li Yi Ma |
| author_facet | Liying Zhu Yabin Hu Guangbo Ren Na Qiao Ziyue Meng Jianbu Wang Yajie Zhao Shibao Li Yi Ma |
| author_sort | Liying Zhu |
| collection | DOAJ |
| description | <italic>Tamarix chinensis</italic> effectively prevents coastal erosion, stabilizes the surface of coastal wetlands, and improves the soil quality of saline-alkali land, playing a crucial role in coastal wetland ecosystem restoration. <italic>Tamarix chinensis</italic> exhibits a wide distribution that is difficult to capture within a single remote sensing image, while its frequent interspersion with other vegetation results in significant intermixing. The characteristics of mixed <italic>tamarix chinensis</italic> vary substantially across remote sensing images from different scenarios, and spectral confusion further complicates the process. These factors hinder the extraction and alignment of mixed <italic>tamarix chinensis</italic> features during classification, resulting in low cross-scene classification accuracy. To address these challenges, this study proposes a deep learning-based cross-domain classification model, ER-GMMD, which leverages features extracted by deep residual networks for different mixed-growth patterns of <italic>tamarix chinensis,</italic> and integrates dual feature alignment to address the cross-scene classification challenges of mixed-species <italic>tamarix chinensis</italic>. Utilizing GF remote sensing images covering the <italic>tamarix chinensis</italic> research area in the Yellow River Delta, along with field survey data, the model achieves precise classification of different mixed <italic>tamarix chinensis</italic> types. Key results include: 1) The proposed model, trained with only 5% of the source domain samples, achieves an overall classification accuracy of 96.52% on the target domain samples, which is a 17.61% improvement compared with the traditional network U-Net without domain adaptation. 2) Compared with domain adaptation algorithms DAN and S-DMM, the proposed ER-GMMD model demonstrates higher accuracy on the constructed dataset, indicating its potential for high-precision classification of mixed vegetation in coastal wetlands. |
| format | Article |
| id | doaj-art-13c36b2b486740d699c364d8fba90b54 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-13c36b2b486740d699c364d8fba90b542025-01-30T00:00:15ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184305431710.1109/JSTARS.2024.352334610829769ER-GMMD: Cross-Scene Remote Sensing Classification Method of <italic>Tamarix chinensis</italic> in the Yellow River EstuaryLiying Zhu0https://orcid.org/0009-0001-1087-935XYabin Hu1https://orcid.org/0000-0003-3826-3239Guangbo Ren2https://orcid.org/0000-0002-3006-9119Na Qiao3Ziyue Meng4https://orcid.org/0009-0008-2318-4486Jianbu Wang5Yajie Zhao6Shibao Li7https://orcid.org/0000-0002-3924-9001Yi Ma8https://orcid.org/0000-0002-5641-3766Lab of Marine Physics and Remote Sensing, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, ChinaLab of Marine Physics and Remote Sensing, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, ChinaLab of Marine Physics and Remote Sensing, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, ChinaChinese National Committee for UNESCO's Man and the Biosphere Programme, Beijing, ChinaFirst Institute of Oceanography, Ministry of Natural Resources, Qingdao, ChinaFirst Institute of Oceanography, Ministry of Natural Resources, Qingdao, ChinaDepartment of Science Research, Yellow River Delta National Nature Reserve Administration, Dongying, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, ChinaLab of Marine Physics and Remote Sensing, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China<italic>Tamarix chinensis</italic> effectively prevents coastal erosion, stabilizes the surface of coastal wetlands, and improves the soil quality of saline-alkali land, playing a crucial role in coastal wetland ecosystem restoration. <italic>Tamarix chinensis</italic> exhibits a wide distribution that is difficult to capture within a single remote sensing image, while its frequent interspersion with other vegetation results in significant intermixing. The characteristics of mixed <italic>tamarix chinensis</italic> vary substantially across remote sensing images from different scenarios, and spectral confusion further complicates the process. These factors hinder the extraction and alignment of mixed <italic>tamarix chinensis</italic> features during classification, resulting in low cross-scene classification accuracy. To address these challenges, this study proposes a deep learning-based cross-domain classification model, ER-GMMD, which leverages features extracted by deep residual networks for different mixed-growth patterns of <italic>tamarix chinensis,</italic> and integrates dual feature alignment to address the cross-scene classification challenges of mixed-species <italic>tamarix chinensis</italic>. Utilizing GF remote sensing images covering the <italic>tamarix chinensis</italic> research area in the Yellow River Delta, along with field survey data, the model achieves precise classification of different mixed <italic>tamarix chinensis</italic> types. Key results include: 1) The proposed model, trained with only 5% of the source domain samples, achieves an overall classification accuracy of 96.52% on the target domain samples, which is a 17.61% improvement compared with the traditional network U-Net without domain adaptation. 2) Compared with domain adaptation algorithms DAN and S-DMM, the proposed ER-GMMD model demonstrates higher accuracy on the constructed dataset, indicating its potential for high-precision classification of mixed vegetation in coastal wetlands.https://ieeexplore.ieee.org/document/10829769/Cross-scene classificationdomain adaptationhigh-resolution remote sensing<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">tamarix chinensis</italic>Yellow River Estuary |
| spellingShingle | Liying Zhu Yabin Hu Guangbo Ren Na Qiao Ziyue Meng Jianbu Wang Yajie Zhao Shibao Li Yi Ma ER-GMMD: Cross-Scene Remote Sensing Classification Method of <italic>Tamarix chinensis</italic> in the Yellow River Estuary IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Cross-scene classification domain adaptation high-resolution remote sensing <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">tamarix chinensis</italic> Yellow River Estuary |
| title | ER-GMMD: Cross-Scene Remote Sensing Classification Method of <italic>Tamarix chinensis</italic> in the Yellow River Estuary |
| title_full | ER-GMMD: Cross-Scene Remote Sensing Classification Method of <italic>Tamarix chinensis</italic> in the Yellow River Estuary |
| title_fullStr | ER-GMMD: Cross-Scene Remote Sensing Classification Method of <italic>Tamarix chinensis</italic> in the Yellow River Estuary |
| title_full_unstemmed | ER-GMMD: Cross-Scene Remote Sensing Classification Method of <italic>Tamarix chinensis</italic> in the Yellow River Estuary |
| title_short | ER-GMMD: Cross-Scene Remote Sensing Classification Method of <italic>Tamarix chinensis</italic> in the Yellow River Estuary |
| title_sort | er gmmd cross scene remote sensing classification method of italic tamarix chinensis italic in the yellow river estuary |
| topic | Cross-scene classification domain adaptation high-resolution remote sensing <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">tamarix chinensis</italic> Yellow River Estuary |
| url | https://ieeexplore.ieee.org/document/10829769/ |
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