A Semisupervised Classification Method for Riverbed Benthic Sediments Using Integrated Superpixel Segmentation and Confident Learning Sample Enhancement
Riverbed sediments are crucial in river ecosystems, significantly impacting water quality, environmental protection, and water resource management. With the increasing ease of obtaining high-resolution river data, integrating multibeam technology and field sampling has become one of the popular meth...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10938952/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849314209630781440 |
|---|---|
| author | Yaxue Wang Yuewen Sun Xiaodong Cui Tianyu Yun Xianhai Bu Fanlin Yang |
| author_facet | Yaxue Wang Yuewen Sun Xiaodong Cui Tianyu Yun Xianhai Bu Fanlin Yang |
| author_sort | Yaxue Wang |
| collection | DOAJ |
| description | Riverbed sediments are crucial in river ecosystems, significantly impacting water quality, environmental protection, and water resource management. With the increasing ease of obtaining high-resolution river data, integrating multibeam technology and field sampling has become one of the popular methods for identifying riverbed sediments. However, limitations in underwater field sampling techniques result in insufficient sampling data, leaving many unlabeled high-resolution acoustic data underutilized, which hinders practical training and deployment of classifiers. To address this issue, this article proposes a sediment sample enhancement method based on superpixel segmentation, confident learning (CL), and active learning (AL) to improve model performance. First, superpixel segmentation is employed to expand field sampling data and increase the number of samples. Next, CL removes noisy labels from the expanded samples, improving data quality. Finally, the centers of superpixel blocks are introduced as unlabeled data and AL techniques are used to select high-quality unlabeled data for labeling. Experiments combining multibeam data from the FuchunRiver region with actual sediment sampling data classify five riverbed sediments. The proposed method expands the original 18 sampling points to 8333 effective sampling points. Comparison results show that the proposed method achieves a classification accuracy of 85.3%, an improvement of 2.9% to 9.9% compared to traditional classification methods, offering a new approach for large-scale benthic environment detection in river basins. |
| format | Article |
| id | doaj-art-55c6ce7b14294d66a2f69d723fe6e17c |
| 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-55c6ce7b14294d66a2f69d723fe6e17c2025-08-20T03:52:32ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118109571097110.1109/JSTARS.2025.355405110938952A Semisupervised Classification Method for Riverbed Benthic Sediments Using Integrated Superpixel Segmentation and Confident Learning Sample EnhancementYaxue Wang0https://orcid.org/0009-0000-3252-0105Yuewen Sun1Xiaodong Cui2https://orcid.org/0000-0001-5453-7346Tianyu Yun3Xianhai Bu4https://orcid.org/0000-0003-4054-0899Fanlin Yang5https://orcid.org/0000-0001-7934-5850College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, ChinaZhejiang Institute of Hydraulics & Estuary (Zhejiang Institute of Marine Planning and Design), Hangzhou, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, ChinaRiverbed sediments are crucial in river ecosystems, significantly impacting water quality, environmental protection, and water resource management. With the increasing ease of obtaining high-resolution river data, integrating multibeam technology and field sampling has become one of the popular methods for identifying riverbed sediments. However, limitations in underwater field sampling techniques result in insufficient sampling data, leaving many unlabeled high-resolution acoustic data underutilized, which hinders practical training and deployment of classifiers. To address this issue, this article proposes a sediment sample enhancement method based on superpixel segmentation, confident learning (CL), and active learning (AL) to improve model performance. First, superpixel segmentation is employed to expand field sampling data and increase the number of samples. Next, CL removes noisy labels from the expanded samples, improving data quality. Finally, the centers of superpixel blocks are introduced as unlabeled data and AL techniques are used to select high-quality unlabeled data for labeling. Experiments combining multibeam data from the FuchunRiver region with actual sediment sampling data classify five riverbed sediments. The proposed method expands the original 18 sampling points to 8333 effective sampling points. Comparison results show that the proposed method achieves a classification accuracy of 85.3%, an improvement of 2.9% to 9.9% compared to traditional classification methods, offering a new approach for large-scale benthic environment detection in river basins.https://ieeexplore.ieee.org/document/10938952/Active learning (AL)confident learning (CL)sample enhancementsediment classification |
| spellingShingle | Yaxue Wang Yuewen Sun Xiaodong Cui Tianyu Yun Xianhai Bu Fanlin Yang A Semisupervised Classification Method for Riverbed Benthic Sediments Using Integrated Superpixel Segmentation and Confident Learning Sample Enhancement IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Active learning (AL) confident learning (CL) sample enhancement sediment classification |
| title | A Semisupervised Classification Method for Riverbed Benthic Sediments Using Integrated Superpixel Segmentation and Confident Learning Sample Enhancement |
| title_full | A Semisupervised Classification Method for Riverbed Benthic Sediments Using Integrated Superpixel Segmentation and Confident Learning Sample Enhancement |
| title_fullStr | A Semisupervised Classification Method for Riverbed Benthic Sediments Using Integrated Superpixel Segmentation and Confident Learning Sample Enhancement |
| title_full_unstemmed | A Semisupervised Classification Method for Riverbed Benthic Sediments Using Integrated Superpixel Segmentation and Confident Learning Sample Enhancement |
| title_short | A Semisupervised Classification Method for Riverbed Benthic Sediments Using Integrated Superpixel Segmentation and Confident Learning Sample Enhancement |
| title_sort | semisupervised classification method for riverbed benthic sediments using integrated superpixel segmentation and confident learning sample enhancement |
| topic | Active learning (AL) confident learning (CL) sample enhancement sediment classification |
| url | https://ieeexplore.ieee.org/document/10938952/ |
| work_keys_str_mv | AT yaxuewang asemisupervisedclassificationmethodforriverbedbenthicsedimentsusingintegratedsuperpixelsegmentationandconfidentlearningsampleenhancement AT yuewensun asemisupervisedclassificationmethodforriverbedbenthicsedimentsusingintegratedsuperpixelsegmentationandconfidentlearningsampleenhancement AT xiaodongcui asemisupervisedclassificationmethodforriverbedbenthicsedimentsusingintegratedsuperpixelsegmentationandconfidentlearningsampleenhancement AT tianyuyun asemisupervisedclassificationmethodforriverbedbenthicsedimentsusingintegratedsuperpixelsegmentationandconfidentlearningsampleenhancement AT xianhaibu asemisupervisedclassificationmethodforriverbedbenthicsedimentsusingintegratedsuperpixelsegmentationandconfidentlearningsampleenhancement AT fanlinyang asemisupervisedclassificationmethodforriverbedbenthicsedimentsusingintegratedsuperpixelsegmentationandconfidentlearningsampleenhancement AT yaxuewang semisupervisedclassificationmethodforriverbedbenthicsedimentsusingintegratedsuperpixelsegmentationandconfidentlearningsampleenhancement AT yuewensun semisupervisedclassificationmethodforriverbedbenthicsedimentsusingintegratedsuperpixelsegmentationandconfidentlearningsampleenhancement AT xiaodongcui semisupervisedclassificationmethodforriverbedbenthicsedimentsusingintegratedsuperpixelsegmentationandconfidentlearningsampleenhancement AT tianyuyun semisupervisedclassificationmethodforriverbedbenthicsedimentsusingintegratedsuperpixelsegmentationandconfidentlearningsampleenhancement AT xianhaibu semisupervisedclassificationmethodforriverbedbenthicsedimentsusingintegratedsuperpixelsegmentationandconfidentlearningsampleenhancement AT fanlinyang semisupervisedclassificationmethodforriverbedbenthicsedimentsusingintegratedsuperpixelsegmentationandconfidentlearningsampleenhancement |