{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T21:18:57Z","timestamp":1767993537049,"version":"3.49.0"},"reference-count":34,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:00:00Z","timestamp":1676937600000},"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>A novel method is proposed to automatically segment water extent using optical data. The key features of this approach are (i) the development of a simple physically based model that utilises only RGB data for water extent segmentation; (ii) the achievement of high accuracy in the results, particularly in the estimation of water surface area and perimeter; (iii) the avoidance of any data training process; (iv) the requirement of minimal computational resources; and (v) the release of an open-source software package that provides both command-line codes and a user-friendly graphical interface, making it accessible for various applications, research, and educational purposes. The physically based model integrates reflectance of the water surface with spectral and quantum interpretation of light. The algorithm was tested on 27 rivers and compared to manually-based delimitation, with a resulting robust segmentation procedure. Quantified errors were RMSE = 11.91 (m2) for surface area, RMSE = 12.25 (m) for perimeter, and RMSE in x: 52 (px), RMSE in y: 93 (px) for centroid location. Processing time was faster for automatic segmentation than manual delimitation, with a time reduction of 40% (case-by-case analysis) and 65% (using all case studies together in one run). Shadows, light spots, and natural and non-natural elements in the field of view may affect the accuracy of results.<\/jats:p>","DOI":"10.3390\/rs15051170","type":"journal-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T01:39:47Z","timestamp":1677029987000},"page":"1170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automatic Segmentation of Water Bodies Using RGB Data: A Physically Based Approach"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6827-2748","authenticated-orcid":false,"given":"Mat\u00edas","family":"Garc\u00eda","sequence":"first","affiliation":[{"name":"Escuela de Ingenier\u00eda en Obras Civiles, Universidad Diego Portales, Av. Ej\u00e9rcito 441, Santiago 8370109, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9300-0816","authenticated-orcid":false,"given":"Hern\u00e1n","family":"Alcayaga","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda en Obras Civiles, Universidad Diego Portales, Av. Ej\u00e9rcito 441, Santiago 8370109, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7242-6559","authenticated-orcid":false,"given":"Alonso","family":"Pizarro","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda en Obras Civiles, Universidad Diego Portales, Av. Ej\u00e9rcito 441, Santiago 8370109, Chile"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"key":"ref_1","unstructured":"Freden, S.C., Mercanti, E.P., and Becker, M.A. (1973). Third Earth Resources Technology Satellite-1 Symposium: Section A\u2013B. Technical presentations, Scientific and Technical Information Office, National Aeronautics and Space Administration."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shen, L., and Li, C. (2010, January 18\u201320). Water body extraction from Landsat ETM+ imagery using adaboost algorithm. Proceedings of the 2010 18th International Conference on Geoinformatics, Beijing, China.","DOI":"10.1109\/GEOINFORMATICS.2010.5567762"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Albertini, C., Gioia, A., Iacobellis, V., and Manfreda, S. (2022). Detection of Surface Water and Floods with Multispectral Satellites. Remote Sens., 14.","DOI":"10.3390\/rs14236005"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.5194\/isprs-archives-XLIII-B2-2020-1189-2020","article-title":"Deep Learning Applied to Water Segmentation","volume":"XLIII-B2-2","author":"Akiyama","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2594","DOI":"10.1080\/01431161.2020.1856964","article-title":"DAU-Net: A novel water areas segmentation structure for remote sensing image","volume":"42","author":"Xia","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/LGRS.2012.2196754","article-title":"Novel Change Detection in SAR Imagery Using Local Connectivity","volume":"10","author":"Wan","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2014.04.020","article-title":"Robust river boundaries extraction of dammed lakes in mountain areas after Wenchuan Earthquake from high resolution SAR images combining local connectivity and ACM","volume":"94","author":"Li","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/LGRS.2010.2051533","article-title":"Automatic Urban Water-Body Detection and Segmentation From Sparse ALSM Data via Spatially Constrained Model-Driven Clustering","volume":"8","author":"Yuan","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ansari, E., Akhtar, M.N., Abdullah, M.N., Othman, W.A.F.W., Abu Bakar, E., Hawary, A.F., and Alhady, S.S.N. (2021). Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia. Sustainability, 13.","DOI":"10.3390\/su13179568"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_14","unstructured":"Teichmann, M.T.T., and Cipolla, R. (2019, January 9\u201312). Convolutional CRFs for Semantic Segmentation. Proceedings of the 30th British Machine Vision Conference 2019, Cardiff, UK."},{"key":"ref_15","unstructured":"Rankin, A., and Matthies, L. (2006, January 27\u201330). Daytime water detection and localization for unmanned ground vehicle autonomous navigation. Proceedings of the 25th Army Science Conference, Orlando, FL, USA."},{"key":"ref_16","first-page":"102497","article-title":"Effectiveness of machine learning methods for water segmentation with ROI as the label: A case study of the Tuul River in Mongolia","volume":"103","author":"Li","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","unstructured":"Sarwal, A., Nett, J., and Simon, D. (2004). Detection of Small Water-Bodies, Perceptek Inc."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Achar, S., Sankaran, B., Nuske, S., Scherer, S., and Singh, S. (2011, January 9\u201313). Self-supervised segmentation of river scenes. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980157"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, X., Jin, J., Lan, Z., Li, C., Fan, M., Wang, Y., Yu, X., and Zhang, Y. (2020). ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features. Remote Sens., 12.","DOI":"10.3390\/rs12020221"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e2020WR028266","DOI":"10.1029\/2020WR028266","article-title":"A Drone-Borne Method to Jointly Estimate Discharge and Manning\u2019s Roughness of Natural Streams","volume":"57","author":"Bandini","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1002\/1520-6378(200102)26:1<67::AID-COL7>3.0.CO;2-4","article-title":"Derivation of the 1964 CIE 10\u00b0 XYZ colour-matching functions and their applicability in photometry","volume":"26","author":"Trezona","year":"2000","journal-title":"Color Res. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Compton, A.H., and Heisenberg, W. (1984). The Physical Principles of the Quantum Theory, Springer.","DOI":"10.1007\/978-3-642-61742-3_10"},{"key":"ref_23","unstructured":"Wu, E.T.H. (2015). Yangton and Yington-A Hypothetical Theory of Everything. Sci. J. Phys., 2013."},{"key":"ref_24","unstructured":"Wu, E.T.H. (2020). Single Slit Diffraction and Double Slit Interference Interpreted by Yangton and Yington Theory. IOSR J. Appl. Phys., 12."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-662-09645-1_1","article-title":"Squeezed States: Basic Principles","volume":"27","author":"Knight","year":"2004","journal-title":"Quantum Squeezing"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mancini, A., Frontoni, E., Zingaretti, P., and Longhi, S. (2015, January 9\u201312). High-resolution mapping of river and estuary areas by using unmanned aerial and surface platforms. Proceedings of the 2015 International Conference on Unmanned Aircraft Systems (ICUAS), Denver, CO, USA.","DOI":"10.1109\/ICUAS.2015.7152333"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Muhadi, N.A., Abdullah, A.F., Bejo, S.K., Mahadi, M.R., and Mijic, A. (2020). Image Segmentation Methods for Flood Monitoring System. Water, 12.","DOI":"10.3390\/w12061825"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"97","DOI":"10.5194\/isprs-archives-XLVI-M-2-2022-97-2022","article-title":"Extracting Water Bodies in Rgb Images Using Deeplabv3+ Algorithm","volume":"Volume XLVI-M-2\u20132022","author":"Harika","year":"2022","journal-title":"The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"105333","DOI":"10.1016\/j.envsoft.2022.105333","article-title":"ATLANTIS: A benchmark for semantic segmentation of waterbody images","volume":"149","author":"Erfani","year":"2022","journal-title":"Environ. Model. Softw."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Ren, D., Emerton, N., Lim, S., and Large, T. (2021, January 20\u201325). Image Restoration for Under-Display Camera. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00906"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"28287","DOI":"10.3390\/s151128287","article-title":"Prototyping a GNSS-Based Passive Radar for UAVs: An Instrument to Classify the Water Content Feature of Lands","volume":"15","author":"Gamba","year":"2015","journal-title":"Sensors"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Issa, H., Stienne, G., Reboul, S., Raad, M., and Faour, G. (2021). Airborne GNSS Reflectometry for Water Body Detection. Remote Sens., 14.","DOI":"10.3390\/rs14010163"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Imam, R., Pini, M., Marucco, G., Dominici, F., and Dovis, F. (2019). UAV-Based GNSS-R for Water Detection as a Support to Flood Monitoring Operations: A Feasibility Study \u2020. Appl. Sci., 10.","DOI":"10.3390\/app10010210"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6652","DOI":"10.1109\/JSTARS.2021.3076003","article-title":"Airborne GNSS-R: A Key Enabling Technology for Environmental Monitoring","volume":"14","author":"Park","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1170\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:38:04Z","timestamp":1760121484000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1170"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,21]]},"references-count":34,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051170"],"URL":"https:\/\/doi.org\/10.3390\/rs15051170","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,21]]}}}