{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:10:00Z","timestamp":1773317400430,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T00:00:00Z","timestamp":1644192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Shallow water bathymetry is critical in understanding and managing marine ecosystems. Bathymetric inversion models using airborne\/satellite multispectral data are an efficient way to retrieve shallow bathymetry due to the affordable cost of airborne\/satellite images and less field work required. With the increasing availability and popularity of unmanned aerial vehicle (UAV) imagery, this paper explores a new approach to obtain bathymetry using UAV visual-band (RGB) images. A combined approach is therefore proposed for retrieving bathymetry from aerial stereo RGB imagery, which is the combination of a new stereo triangulation method (an improved projection image based two-medium stereo triangulation method) and spectral inversion models. In general, the inversion models require some bathymetry reference points, which are not always feasible in many scenarios, and the proposed approach employs a new stereo triangulation method to obtain reliable bathymetric points, which act as the reference points of the inversion models. Using various numbers of triangulation points as the reference points together with a Geographical Weighted Regression (GWR) model, a series of experiments were conducted using UAV RGB images of a small island, and the results were validated against LiDAR points. The promising results indicate that the proposed approach is an efficient technique for shallow water bathymetry retrieval, and together with UAV platforms, it could be deployed easily to conduct a broad range of applications within marine environments.<\/jats:p>","DOI":"10.3390\/rs14030760","type":"journal-article","created":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T08:38:48Z","timestamp":1644223128000},"page":"760","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Combined Approach for Retrieving Bathymetry from Aerial Stereo RGB Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4171-5024","authenticated-orcid":false,"given":"Jiali","family":"Wang","sequence":"first","affiliation":[{"name":"College of Information, Shanghai Ocean University, 999 Hucheng Huanlu Road, Shanghai 201308, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4393-6250","authenticated-orcid":false,"given":"Ming","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information, Shanghai Ocean University, 999 Hucheng Huanlu Road, Shanghai 201308, China"}]},{"given":"Weidong","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Marine Science, Shanghai Ocean University, 999 Hucheng Huanlu Road, Shanghai 201308, China"}]},{"given":"Liting","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Marine Science, Shanghai Ocean University, 999 Hucheng Huanlu Road, Shanghai 201308, China"}]},{"given":"Yasong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Marine Science, Shanghai Ocean University, 999 Hucheng Huanlu Road, Shanghai 201308, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zhang, H., Shi, C., and Zhao, J. 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