{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:11:09Z","timestamp":1773317469112,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T00:00:00Z","timestamp":1685232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007694","name":"Korea Agency for Infrastructure Technology Advancement","doi-asserted-by":"publisher","award":["22DPIW-C153746-04"],"award-info":[{"award-number":["22DPIW-C153746-04"]}],"id":[{"id":"10.13039\/501100007694","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Passive remote sensing is a practical and widely used method for bathymetry mapping in shallow rivers. However, the accuracy of this approach is limited because of different riverbed types; therefore, it is important to classify the riverbed types for improving bathymetry mapping accuracy and providing useful information for fluvial systems. In this study, we proposed a Gaussian mixture model (GMM)-based clustering method that utilizes hyperspectral imagery to classify riverbed types without sampling the bed material. We evaluated the proposed method in two shallow streams with different bed mixture conditions: (i) sand and vegetation and (ii) sand and moss-covered sand. The results showed that the GMM method accurately identified the spectral variability caused by diverse riverbed materials, enabling the precise classification of riverbed types. Moreover, by combining the GMM method with optimal band ratio analysis, we observed a reduction in error for the bathymetry mapping results by approximately 0.05 to 0.07 m. While our proposed method exhibits potential applications in various river environments, further research is needed to validate its effectiveness in classifying more complex riverbed types and conditions. Overall, our study findings suggest that the GMM-based clustering method using hyperspectral imagery is a promising tool for improving bathymetry mapping accuracy and classifying riverbed types in shallow rivers.<\/jats:p>","DOI":"10.3390\/rs15112803","type":"journal-article","created":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T15:29:52Z","timestamp":1685287792000},"page":"2803","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Unsupervised Classification of Riverbed Types for Bathymetry Mapping in Shallow Rivers Using UAV-Based Hyperspectral Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1921-5370","authenticated-orcid":false,"given":"Siyoon","family":"Kwon","sequence":"first","affiliation":[{"name":"Institute of Construction and Environmental Engineering, Seoul National University, Seoul 08826, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8376-4978","authenticated-orcid":false,"given":"Yeonghwa","family":"Gwon","sequence":"additional","affiliation":[{"name":"Department of Civil & Environmental Engineering, Dankook University, Yongin 16890, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4536-767X","authenticated-orcid":false,"given":"Dongsu","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Civil & Environmental Engineering, Dankook University, Yongin 16890, Republic of Korea"}]},{"given":"Il Won","family":"Seo","sequence":"additional","affiliation":[{"name":"Institute of Construction and Environmental Engineering, Seoul National University, Seoul 08826, Republic of Korea"}]},{"given":"Hojun","family":"You","sequence":"additional","affiliation":[{"name":"K-Water Institute, Daejeon 34350, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,28]]},"reference":[{"key":"ref_1","first-page":"440","article-title":"Bathymetric and Geochemical Analysis of Lake Al-Saad, Abha, Kingdom of Saudi Arabia Using Geoinformatics Technology","volume":"6","author":"Mallick","year":"2014","journal-title":"J. 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