{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:46:09Z","timestamp":1780591569847,"version":"3.54.1"},"reference-count":75,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T00:00:00Z","timestamp":1606953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006505","name":"Engineer Research and Development Center","doi-asserted-by":"publisher","award":["XXX"],"award-info":[{"award-number":["XXX"]}],"id":[{"id":"10.13039\/100006505","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Nearshore morphology is a key driver in wave breaking and the resulting nearshore circulation, recreational safety, and nutrient dispersion. Morphology persists within the nearshore in specific shapes that can be classified into equilibrium states. Equilibrium states convey qualitative information about bathymetry and relevant physical processes. While nearshore bathymetry is a challenge to collect, much information about the underlying bathymetry can be gained from remote sensing of the surfzone. This study presents a new method to automatically classify beach state from Argus daytimexposure imagery using a machine learning technique called convolutional neural networks (CNNs). The CNN processed imagery from two locations: Narrabeen, New South Wales, Australia and Duck, North Carolina, USA. Three different CNN models are examined, one trained at Narrabeen, one at Duck, and one trained at both locations. Each model was tested at the location where it was trained in a self-test, and the single-beach models were tested at the location where it was not trained in a transfer-test. For the self-tests, skill (as measured by the F-score) was comparable to expert agreement (CNN F-values at Duck = 0.80 and Narrabeen = 0.59). For the transfer-tests, the CNN model skill was reduced by 24\u201348%, suggesting the algorithm requires additional local data to improve transferability performance. Transferability tests showed that comparable F-scores (within 10%) to the self-trained cases can be achieved at both locations when at least 25% of the training data is from each site. This suggests that if applied to additional locations, a CNN model trained at one location may be skillful at new sites with limited new imagery data needed. Finally, a CNN visualization technique (Guided-Grad-CAM) confirmed that the CNN determined classifications using image regions (e.g., incised rip channels, terraces) that were consistent with beach state labelling rules.<\/jats:p>","DOI":"10.3390\/rs12233953","type":"journal-article","created":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T11:15:43Z","timestamp":1606994143000},"page":"3953","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Beach State Recognition Using Argus Imagery and Convolutional Neural Networks"],"prefix":"10.3390","volume":"12","author":[{"given":"Ashley N.","family":"Ellenson","sequence":"first","affiliation":[{"name":"School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97330, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0606-7225","authenticated-orcid":false,"given":"Joshua A.","family":"Simmons","sequence":"additional","affiliation":[{"name":"Water Research Laboratory, School of Civil and Environmental Engineering, UNSW Sydney, Sydney, NSW 2093, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Greg W.","family":"Wilson","sequence":"additional","affiliation":[{"name":"College of Earth, Ocean and Atmospheric Sciences, Oregon State University, Corvallis, OR 97330, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tyler J.","family":"Hesser","sequence":"additional","affiliation":[{"name":"United States Army Corps Engineer Research and Development Center, Vicksburg, MS 39180, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0082-8444","authenticated-orcid":false,"given":"Kristen D.","family":"Splinter","sequence":"additional","affiliation":[{"name":"Water Research Laboratory, School of Civil and Environmental Engineering, UNSW Sydney, Sydney, NSW 2093, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Holman, R.A., Symonds, G., Thornton, E.B., and Ranasinghe, R. (2006). Rip spacing and persistence on an embayed beach. J. Geophys. Res. Ocean., 111.","DOI":"10.1029\/2005JC002965"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.margeo.2006.10.029","article-title":"Observations of rip spacing, persistence and mobility at a long, straight coastline","volume":"236","author":"Turner","year":"2007","journal-title":"Mar. Geol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wilson, G.W., \u00d6zkan-Haller, H.T., and Holman, R.A. (2010). Data assimilation and bathymetric inversion in a two-dimensional horizontal surf zone model. J. Geophys. Res. Ocean., 115.","DOI":"10.1029\/2010JC006286"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1126\/science.181.4094.20","article-title":"The coastal challenge","volume":"181","author":"Inman","year":"1973","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Grant, S.B., Kim, J.H., Jones, B.H., Jenkins, S.A., Wasyl, J., and Cudaback, C. (2005). Surf zone entrainment, along-shore transport, and human health implications of pollution from tidal outlets. J. Geophys. Res. Ocean., 110.","DOI":"10.1029\/2004JC002401"},{"key":"ref_6","first-page":"283","article-title":"Rip Current Prediction: Development, Validation, and Evaluation of an Operational Tool","volume":"29","author":"Austin","year":"2013","journal-title":"J. Coast. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.earscirev.2016.09.008","article-title":"Rip current types, circulation and hazard","volume":"163","author":"Castelle","year":"2016","journal-title":"Earth-Sci. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/0025-3227(84)90008-2","article-title":"Morphodynamic variability of surf zones and beaches: A synthesis","volume":"56","author":"Wright","year":"1984","journal-title":"Mar. Geol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.ocecoaman.2017.04.004","article-title":"Coastal erosion and the United States national flood insurance program","volume":"156","author":"Leatherman","year":"2018","journal-title":"Ocean Coast. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104902","DOI":"10.1016\/j.ocecoaman.2019.104902","article-title":"Social, geomorphic, and climatic factors driving US coastal city vulnerability to storm surge flooding","volume":"181","author":"Helderop","year":"2019","journal-title":"Ocean Coast. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.margeo.2007.02.018","article-title":"Rip currents, mega-cusps, and eroding dunes","volume":"240","author":"Thornton","year":"2007","journal-title":"Mar. Geol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.geomorph.2015.03.006","article-title":"Impact of the winter 2013\u20132014 series of severe Western Europe storms on a double-barred sandy coast: Beach and dune erosion and megacusp embayments","volume":"238","author":"Castelle","year":"2015","journal-title":"Geomorphology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.coastaleng.2007.01.003","article-title":"The history and technical capabilities of Argus","volume":"54","author":"Holman","year":"2007","journal-title":"Coast. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11575","DOI":"10.1029\/JC095iC07p11575","article-title":"The spatial and temporal variability of sand bar morphology","volume":"95","author":"Lippmann","year":"1990","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/0025-3227(85)90123-9","article-title":"Short-term changes in the morphodynamic states of beaches and surf zones: An empirical predictive model","volume":"62","author":"Wright","year":"1985","journal-title":"Mar. Geol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/j.coastaleng.2004.07.018","article-title":"Morphodynamics of intermediate beaches: A video imaging and numerical modelling study","volume":"51","author":"Ranasinghe","year":"2004","journal-title":"Coast. Eng."},{"key":"ref_17","unstructured":"Strauss, D., Tomlinson, R., and Hughes, L. (2006, January 10\u201313). Numerical modelling and video analysis of intermediate beach state transitions. Proceedings of the 7th International Conference on Hydroscience and Engineering, Philadelphia, PA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Plant, N.G., Holland, K.T., and Holman, R.A. (2006). A dynamical attractor governs beach response to storms. Geophys. Res. Lett., 33.","DOI":"10.1029\/2006GL027105"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.margeo.2006.10.022","article-title":"Coupling video imaging and numerical modelling for the study of inlet morphodynamics","volume":"236","author":"Siegle","year":"2007","journal-title":"Mar. Geol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Splinter, K.D., Holman, R.A., and Plant, N.G. (2011). A behavior-oriented dynamic model for sandbar migration and 2DH evolution. J. Geophys. Res. Ocean., 116.","DOI":"10.1029\/2010JC006382"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5645","DOI":"10.1002\/2017GL073094","article-title":"Mechanisms controlling the complete accretionary beach state sequence","volume":"44","author":"Dubarbier","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/S0278-4343(02)00235-2","article-title":"Video observations of nearshore bar behaviour. Part 2: Alongshore non-uniform variability","volume":"23","author":"Ruessink","year":"2003","journal-title":"Cont. Shelf Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.margeo.2007.06.001","article-title":"Double bar beach dynamics on the high-energy meso-macrotidal French Aquitanian Coast: A review","volume":"245","author":"Castelle","year":"2007","journal-title":"Mar. Geol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1016\/j.csr.2010.12.018","article-title":"State dynamics of a double sandbar system","volume":"31","author":"Price","year":"2011","journal-title":"Cont. Shelf Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.margeo.2010.12.002","article-title":"Dynamics of single-barred embayed beaches","volume":"280","author":"Ojeda","year":"2011","journal-title":"Mar. Geol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.geomorph.2010.11.004","article-title":"Dynamics of a nearshore bar system in the northern Adriatic: A video-based morphological classification","volume":"126","author":"Armaroli","year":"2011","journal-title":"Geomorphology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"458","DOI":"10.2112\/SI65-078.1","article-title":"Video monitoring nearshore sandbar morphodynamics on a partially engineered embayed beach","volume":"65","author":"Morichon","year":"2013","journal-title":"J. Coast. Res."},{"key":"ref_28","first-page":"197","article-title":"Rip currents and beach hazards: Their impact on public safety and implications for coastal management","volume":"12","author":"Short","year":"1994","journal-title":"J. Coast. Res."},{"key":"ref_29","first-page":"785","article-title":"The Effect of Tide Range on Beach Morphodynamics and Morphology: A Conceptual Beach Model","volume":"9","author":"Masselink","year":"1993","journal-title":"J. Coast. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.margeo.2013.09.005","article-title":"Applicability of parametric beach morphodynamic state classification on embayed beaches","volume":"346","author":"Loureiro","year":"2013","journal-title":"Mar. Geol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1029\/JC094iC01p00995","article-title":"Quantification of sand bar morphology: A video technique based on wave dissipation","volume":"94","author":"Lippmann","year":"1989","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3418","DOI":"10.1109\/TGRS.2006.877758","article-title":"Objective Beach-State Classification From Optical Sensing of Cross-Shore Dissipation Profiles","volume":"44","author":"Browne","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"16969","DOI":"10.1029\/1999JC000167","article-title":"Effect of hydrodynamics and bathymetry on video estimates of nearshore sandbar position","volume":"106","author":"Ruessink","year":"2001","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.margeo.2015.06.010","article-title":"Sandbar straightening under wind-sea and swell forcing","volume":"368","author":"Contardo","year":"2015","journal-title":"Mar. Geol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Splinter, K.D., Harley, M.D., and Turner, I.L. (2018). Remote sensing is changing our view of the coast: Insights from 40 years of monitoring at Narrabeen-Collaroy, Australia. Remote Sens., 10.","DOI":"10.3390\/rs10111744"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.coastaleng.2007.01.009","article-title":"The role of video imagery in predicting daily to monthly coastal evolution","volume":"54","author":"Smit","year":"2007","journal-title":"Coast. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1146\/annurev-marine-121211-172408","article-title":"Remote Sensing of the Nearshore","volume":"5","author":"Holman","year":"2013","journal-title":"Annu. Rev. Mar. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.ocemod.2015.08.002","article-title":"Significant wave height record extension by neural networks and reanalysis wind data","volume":"94","author":"Peres","year":"2015","journal-title":"Ocean Model."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.coastaleng.2019.03.006","article-title":"Distribution of individual wave overtopping volumes on mound breakwaters","volume":"149","author":"Molines","year":"2019","journal-title":"Coast. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"103595","DOI":"10.1016\/j.coastaleng.2019.103595","article-title":"An application of a machine learning algorithm to determine and describe error patterns within wave model output","volume":"157","author":"Ellenson","year":"2020","journal-title":"Coast. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"103689","DOI":"10.1016\/j.coastaleng.2020.103689","article-title":"Deep learning video analysis as measurement technique in physical models","volume":"158","year":"2020","journal-title":"Coast. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"103593","DOI":"10.1016\/j.coastaleng.2019.103593","article-title":"Optical wave gauging using deep neural networks","volume":"155","author":"Buscombe","year":"2020","journal-title":"Coast. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1002\/esp.4760","article-title":"SediNet: A configurable deep learning model for mixed qualitative and quantitative optical granulometry","volume":"45","author":"Buscombe","year":"2020","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"103572","DOI":"10.1016\/j.coastaleng.2019.103572","article-title":"Physical model of scour at the toe of rock armoured structures","volume":"154","author":"Hoonhout","year":"2019","journal-title":"Coast. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.coastaleng.2015.07.010","article-title":"An automated method for semantic classification of regions in coastal images","volume":"105","author":"Hoonhout","year":"2015","journal-title":"Coast. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104528","DOI":"10.1016\/j.envsoft.2019.104528","article-title":"CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery","volume":"122","author":"Vos","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Buscombe, D., and Carini, R.J. (2019). A Data-Driven Approach to Classifying Wave Breaking in Infrared Imagery. Remote Sens., 11.","DOI":"10.20944\/preprints201903.0283.v1"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.5194\/nhess-19-2295-2019","article-title":"Ensemble models from machine learning: An example of wave runup and coastal dune erosion","volume":"19","author":"Beuzen","year":"2019","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Birkemeier, W.A., DeWall, A.E., Gorbics, C.S., and Miller, H.C. (1981). A User\u2019s Guide to CERC\u2019s Field Research Facility, Coastal Engineering Research Center. Technical Report.","DOI":"10.5962\/bhl.title.48249"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1111\/j.1753-318X.2008.00013.x","article-title":"An investigation of the link between beach morphology and wave climate at Duck, NC, USA","volume":"1","author":"Reeve","year":"2008","journal-title":"J. Flood Risk Manag."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Stauble, D.K. (1992). Long-Term Profile and Sediment Morphodynamics: Field Research Facility Case History, Coastal Engineering Research Center. Technical Report 92\u201397.","DOI":"10.5962\/bhl.title.48254"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.margeo.2004.04.017","article-title":"Quantification of nearshore morphology based on video imaging","volume":"208","author":"Alexander","year":"2004","journal-title":"Mar. Geol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"160024","DOI":"10.1038\/sdata.2016.24","article-title":"A multi-decade dataset of monthly beach profile surveys and inshore wave forcing at Narrabeen, Australia","volume":"3","author":"Turner","year":"2016","journal-title":"Sci. Data"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Harley, M.D., Turner, I.L., Short, A.D., and Ranasinghe, R. (2011). A reevaluation of coastal embayment rotation: The dominance of cross-shore versus alongshore sediment transport processes, Collaroy-Narrabeen Beach, southeast Australia. J. Geophys. Res. Earth Surf., 116.","DOI":"10.1029\/2011JF001989"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/48.557542","article-title":"Practical use of video imagery in nearshore oceanographic field studies","volume":"22","author":"Holland","year":"1997","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.patcog.2019.04.009","article-title":"Reshaping inputs for convolutional neural network: Some common and uncommon methods","volume":"93","author":"Ghosh","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_57","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_58","unstructured":"Perez, L., and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_60","unstructured":"Simon, M., Rodner, E., and Denzler, J. (2016). ImageNet pre-trained models with batch normalization. arXiv."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_62","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., and Riedmiller, M. (2015). Striving for simplicity: The all convolutional net. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Visualizing and Understanding Convolutional Networks. Computer Vision\u2014ECCV 2014, Springer International Publishing.","DOI":"10.1007\/978-3-319-10602-1"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 27\u201330). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1093\/bioinformatics\/16.5.412","article-title":"Assessing the accuracy of prediction algorithms for classification: An overview","volume":"16","author":"Baldi","year":"2000","journal-title":"Bioinformatics"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Chen, L., Wang, S., Fan, W., Sun, J., and Naoi, S. (2015, January 3\u20136). Beyond human recognition: A CNN-based framework for handwritten character recognition. Proceedings of the 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ACPR.2015.7486592"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1002\/2014JC010329","article-title":"Shoreline variability from days to decades: Results of long-term video imaging","volume":"120","author":"Pianca","year":"2015","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_68","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2015). Object Detectors Emerge in Deep Scene CNNs. arXiv."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/S0025-3227(00)00168-7","article-title":"Intertidal beach slope predictions compared to field data","volume":"173","author":"Madsen","year":"2001","journal-title":"Mar. Geol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0025-3227(97)00019-4","article-title":"Intertidal beach profile estimation using video images","volume":"140","author":"Plant","year":"1997","journal-title":"Mar. Geol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.cageo.2009.05.003","article-title":"Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification","volume":"36","author":"Balaguer","year":"2010","journal-title":"Comput. Geosci."},{"key":"ref_72","first-page":"1","article-title":"Introduction to geostatistics and variogram analysis","volume":"1","author":"Bohling","year":"2005","journal-title":"Kans. Geol. Surv."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/10095020.2015.1116206","article-title":"Evaluation of semivariogram features for object-based image classification","volume":"18","author":"Wu","year":"2015","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_74","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3953\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:40:58Z","timestamp":1760179258000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3953"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,3]]},"references-count":75,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12233953"],"URL":"https:\/\/doi.org\/10.3390\/rs12233953","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,3]]}}}