{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:08:50Z","timestamp":1780675730376,"version":"3.54.1"},"reference-count":84,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T00:00:00Z","timestamp":1602115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012046","name":"Vietnam Academy of Science and Technology","doi-asserted-by":"publisher","award":["UQSNMT.02\/20-21"],"award-info":[{"award-number":["UQSNMT.02\/20-21"]}],"id":[{"id":"10.13039\/100012046","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The natural wetland areas in Vietnam, which are transition areas from inland and ocean, play a crucial role in minimizing coastal hazards; however, during the last two decades, about 64% of these areas have been converted from the natural wetland to the human-made wetland. It is anticipated that the conversion rate continues to increase due to economic development and urbanization. Therefore, monitoring and assessment of the wetland are essential for the coastal vulnerability assessment and geo-ecosystem management. The aim of this study is to propose and verify a new deep learning approach to interpret 9 of 19 coastal wetland types classified in the RAMSAR and MONRE systems for the Tien Yen estuary of Vietnam. Herein, a Resnet framework was integrated into the U-Net to optimize the performance of the proposed deep learning model. The Sentinel-2, ALOS-DEM, and NOAA-DEM satellite images were used as the input data, whereas the output is the predefined nine wetland types. As a result, two ResU-Net models using Adam and RMSprop optimizer functions show the accuracy higher than 85%, especially in forested intertidal wetlands, aquaculture ponds, and farm ponds. The better performance of these models was proved, compared to Random Forest and Support Vector Machine methods. After optimizing the ResU-Net models, they were also used to map the coastal wetland areas correctly in the northeastern part of Vietnam. The final model can potentially update new wetland types in the southern parts and islands in Vietnam towards wetland change monitoring in real time.<\/jats:p>","DOI":"10.3390\/rs12193270","type":"journal-article","created":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T10:22:25Z","timestamp":1602152545000},"page":"3270","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam"],"prefix":"10.3390","volume":"12","author":[{"given":"Kinh Bac","family":"Dang","sequence":"first","affiliation":[{"name":"Faculty of Geography, VNU University of Science, 334 Nguyen Trai, Thanh Xuan, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manh Ha","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Geography Institute, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Cau Giay, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duc Anh","family":"Nguyen","sequence":"additional","affiliation":[{"name":"SKYMAP High Technology Co., Ltd., No.6, 40\/2\/1, Ta Quang Buu, Hai Ba Trung, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thi Thanh Hai","family":"Phan","sequence":"additional","affiliation":[{"name":"Faculty of Geography, VNU University of Science, 334 Nguyen Trai, Thanh Xuan, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tuan Linh","family":"Giang","sequence":"additional","affiliation":[{"name":"SKYMAP High Technology Co., Ltd., No.6, 40\/2\/1, Ta Quang Buu, Hai Ba Trung, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hoang Hai","family":"Pham","sequence":"additional","affiliation":[{"name":"Geography Institute, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Cau Giay, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thu Nhung","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Geography Institute, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Cau Giay, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thi Thuy Van","family":"Tran","sequence":"additional","affiliation":[{"name":"Geography Institute, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Cau Giay, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5161-6479","authenticated-orcid":false,"given":"Dieu Tien","family":"Bui","sequence":"additional","affiliation":[{"name":"GIS Group, Department of Business and IT, School of Business, University of South-Eastern Norway, Gullbringvegen 36, N-3800 B\u00f8 i Telemark, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,8]]},"reference":[{"key":"ref_1","unstructured":"Dugan, P.J. (1990). Wetland Conservation: A Review of Current Issues and Action, IUCN."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.ocecoaman.2014.02.005","article-title":"Van Der Ocean & Coastal Management Long term anthropogenic changes and ecosystem service consequences in the northern part of the complex Rhine-Meuse estuarine system","volume":"92","author":"Paalvast","year":"2014","journal-title":"Ocean Coast. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ocecoaman.2017.02.021","article-title":"Assessing risk of estuarine ecosystem collapse","volume":"140","author":"Mahoney","year":"2017","journal-title":"Ocean Coast. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, T., and Gao, X. (2016). Ecosystem services valuation of Lakeside Wetland park beside Chaohu Lake in China. Water (Switzerland), 8.","DOI":"10.3390\/w8070301"},{"key":"ref_5","unstructured":"Russi, D., ten Brink, P., Farmer, A., Bandura, T., Coates, D., Dorster, J., Kumar, R., and Davidson, N. (2012). The Economics of Ecosystems and Biodiversity for Water and Wetlands, IEEP London and Brussels."},{"key":"ref_6","unstructured":"RAMSA (2020, October 08). Wetlands: A global disappearing act. Available online: https:\/\/www.ramsar.org\/document\/ramsar-fact-sheet-3-wetlands-a-global-disappearing-act."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1071\/MF14173","article-title":"How much wetland has the world lost? Long-term and recent trends in global wetland area","volume":"65","author":"Davidson","year":"2014","journal-title":"Mar. Freshw. Res."},{"key":"ref_8","unstructured":"CBD (2015). Wetlands and Ecosystem Services, United Nations."},{"key":"ref_9","unstructured":"Duc, L.D. (1993). Wetland Reserves in Vietnam (In Vietnamese), Agricultural Publishing House. Centre for."},{"key":"ref_10","unstructured":"Buckton, S.T., Cu, N., Quynh, H.Q., and Tu, N.D. (1989). The Conservation of Key Wetland Sites in the Mekong Delta, BirdLife International Vietnam Porgramme."},{"key":"ref_11","unstructured":"Hawkins, S., To, P.X., Phuong, P.X., Thuy, P.T., Tu, N.D., Cuong, C.V., Brown, S., Dart, P., Robertson, S., and Vu, N. (2010). Roots in the Water: Legal Frameworks for Mangrove PES in Vietnam, Katoomba Group\u2019s Legal Initiative Country Study Series."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.ecolind.2014.06.012","article-title":"Wetland ecosystem service values and shrimp aquaculture relationships in Can Gio, Vietnam","volume":"46","author":"McDonough","year":"2014","journal-title":"Ecol. Indic."},{"key":"ref_13","unstructured":"Eames, J.C. (1996). The Conservation of Key Coastal Wetland Sites in the Red River Delta, BirdLife International. Hanoi BirdLife International Programme."},{"key":"ref_14","unstructured":"Naganuma, K. (2014). Environmental planning of Quang Ninh province to 2020 vision to 2030. Quang Ninh Prov. People\u2019s Comm."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.gsf.2015.07.003","article-title":"Machine learning in geosciences and remote sensing","volume":"7","author":"Lary","year":"2016","journal-title":"Geosci. Front."},{"key":"ref_16","first-page":"148","article-title":"Crop Production - Ensemble Machine Learning Model for Prediction","volume":"5","author":"Balakrishnan","year":"2016","journal-title":"Int. J. Comput. Sci. Softw. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0215676","article-title":"Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields","volume":"14","author":"Ma","year":"2019","journal-title":"PLoS ONE"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.envsoft.2019.01.015","article-title":"Application of a hybrid neural-fuzzy inference system for mapping crop suitability areas and predicting rice yields","volume":"114","author":"Dang","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shi, Q., Li, W., Tao, R., Sun, X., and Gao, L. (2019). Ship Classification Based on Multifeature Ensemble with Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11040419"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1111\/2041-210X.13132","article-title":"A convolutional neural network for detecting sea turtles in drone imagery","volume":"10","author":"Gray","year":"2019","journal-title":"Methods Ecol. Evol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Guo, Q., Jin, S., Li, M., Yang, Q., Xu, K., Ju, Y., Zhang, J., Xuan, J., Liu, J., and Su, Y. (2020). Application of deep learning in ecological resource research: Theories, methods, and challenges. Sci. China Earth Sci., 2172.","DOI":"10.1007\/s11430-019-9584-9"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"11824","DOI":"10.1109\/ACCESS.2020.2965231","article-title":"A Convolutional Neural Network for Coastal Classification Based on ALOS and NOAA Satellite Data","volume":"8","author":"Dang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gebrehiwot, A., Hashemi-Beni, L., Thompson, G., Kordjamshidi, P., and Langan, T.E. (2019). Deep convolutional neural network for flood extent mapping using unmanned aerial vehicles data. Sensors (Switzerland), 19.","DOI":"10.3390\/s19071486"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.agsy.2019.03.015","article-title":"Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia","volume":"173","author":"Feng","year":"2019","journal-title":"Agric. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dang, K.B., Windhorst, W., Burkhard, B., and M\u00fcller, F. (2018). A Bayesian Belief Network \u2013 Based approach to link ecosystem functions with rice provisioning ecosystem services. Ecol. Indic.","DOI":"10.1016\/j.ecolind.2018.04.055"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Guo, M., Li, J., Sheng, C., Xu, J., and Wu, L. (2017). A review of wetland remote sensing. Sensors (Switzerland), 17.","DOI":"10.3390\/s17040777"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mahdianpari, M., Granger, J.E., Mohammadimanesh, F., Salehi, B., Brisco, B., Homayouni, S., Gill, E., Huberty, B., and Lang, M. (2020). Meta-analysis of wetland classification using remote sensing: A systematic review of a 40-year trend in North America. Remote Sens., 12.","DOI":"10.3390\/rs12111882"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1023\/A:1020908432489","article-title":"Satellite remote sensing of wetlands","volume":"10","author":"Ozesmi","year":"2002","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_29","unstructured":"Davis, T.J. (1994). The Ramsar Convention Manual: A Guide for the Convention on Wetlands of International Importance Especially as waterfowl Habitat, Ramsar Convention Bureau."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tian, S., Zhang, X., Tian, J., and Sun, Q. (2016). Random forest classification of wetland landcovers from multi-sensor data in the arid region of Xinjiang, China. Remote Sens., 8.","DOI":"10.3390\/rs8110954"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2017.05.010","article-title":"Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery","volume":"130","author":"Mahdianpari","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","first-page":"18","article-title":"A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological","volume":"11","author":"Chen","year":"2019","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.isprsjprs.2018.03.006","article-title":"Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification","volume":"139","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.rse.2018.04.001","article-title":"A fusion-based methodology for meteorological drought estimation using remote sensing data","volume":"211","author":"Alizadeh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Garg, L., Shukla, P., Singh, S.K., Bajpai, V., and Yadav, U. (2019, January 25\u201327). Land use land cover classification from satellite imagery using mUnet: A modified UNET architecture. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019), Prague, Czech Republic.","DOI":"10.5220\/0007370603590365"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., and Al-Zawi, S. (2017, January 21\u201323). Understanding of a convolutional neural network. Proceedings of the 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey.","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Stoian, A., Poulain, V., Inglada, J., Poughon, V., and Derksen, D. (2019). Land cover maps production with high resolution satellite image time series and convolutional neural networks: Adaptations and limits for operational systems. Remote Sens., 11.","DOI":"10.20944\/preprints201906.0270.v2"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, B., Li, Y., Li, G., and Liu, A. (2019). A spectral feature based convolutional neural network for classification of sea surface oil spill. ISPRS Int. J. Geo-Information, 8.","DOI":"10.3390\/ijgi8040160"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pouliot, D., Latifovic, R., Pasher, J., and Duffe, J. (2019). Assessment of convolution neural networks for wetland mapping with landsat in the central Canadian boreal forest region. Remote Sens., 11.","DOI":"10.3390\/rs11070772"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"DeLancey, E.R., Simms, J.F., Mahdianpari, M., Brisco, B., Mahoney, C., and Kariyeva, J. (2020). Comparing deep learning and shallow learning for large-scalewetland classification in Alberta, Canada. Remote Sens., 12.","DOI":"10.3390\/rs12010002"},{"key":"ref_41","unstructured":"Gordana, K., and Avdan, U. (2019). AVDAN Evaluating Sentinel-2 Red-Edge Bands for Wetland Classification. Proceedings, 18."},{"key":"ref_42","first-page":"102009","article-title":"Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands, South Africa","volume":"86","author":"Slagter","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, X., Gao, X., Zhang, Y., Fei, X., Chen, Z., Wang, J., Zhang, Y., Lu, X., and Zhao, H. (2019). Land-cover classification of coastal wetlands using the RF algorithm for Worldview-2 and Landsat 8 images. Remote Sens., 11.","DOI":"10.3390\/rs11161927"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17148\/IJARCCE.2018.71201","article-title":"A Convolutional Neural Network with K-Neareast Neighbor for Image Classification","volume":"7","author":"Abubakar","year":"2018","journal-title":"Int. J. Adv. Res. Comput. Commun. Eng. (IJARCCE)"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.rse.2006.07.014","article-title":"Neural network estimation of LAI, fAPAR, fCover and LAI\u00d7Cab, from top of canopy MERIS reflectance data: Principles and validation","volume":"105","author":"Bacour","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2018.10.006","article-title":"Prediction of drought-induced reduction of agricultural productivity in Chile from MODIS, rainfall estimates, and climate oscillation indices","volume":"219","author":"Zambrano","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Feng, Q., Yang, J., Zhu, D., Liu, J., Guo, H., Bayartungalag, B., and Li, B. (2019). Integrating multitemporal Sentinel-1\/2 data for coastal land cover classification using a multibranch convolutional neural network: A case of the Yellow River Delta. Remote Sens., 11.","DOI":"10.3390\/rs11091006"},{"key":"ref_48","first-page":"1689","article-title":"Overview of Wetlands Status in Viet Nam Following 15 Years of Ramsar Convention Implementation Table","volume":"369","author":"Amaral","year":"2013","journal-title":"J. Petrol."},{"key":"ref_49","first-page":"67","article-title":"Importance of Tien Yen Estuary (Northern Vietnam) for early-stage Nuchequula nuchalis (Temminck & Schlegel, 1845)","volume":"15","author":"Tran","year":"2016","journal-title":"Chiang Mai Univ. J. Nat. Sci."},{"key":"ref_50","first-page":"6","article-title":"Primary assessment of water quality and phytoplankton diversity in Dong Rui Wetland, Tien Yen District, Quang Ninh Province","volume":"33","author":"Nguyen","year":"2017","journal-title":"VNU J. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"421","DOI":"10.3390\/rs6010421","article-title":"Improved accuracy of chlorophyll-a concentration estimates from MODIS Imagery using a two-band ratio algorithm and geostatistics: As applied to the monitoring of eutrophication processes over Tien Yen Bay (Northern Vietnam)","volume":"6","author":"Ha","year":"2013","journal-title":"Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"De Groot, D., Brander, L., and Finlayson, M. (2016). Wetland Ecosystem Services. Wetl. B., 1\u201311.","DOI":"10.1007\/978-94-007-6172-8_66-1"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"He, Z., He, D., Mei, X., and Hu, S. (2019). Wetland classification based on a new efficient generative adversarial network and Jilin-1 satellite image. Remote Sens., 11.","DOI":"10.3390\/rs11202455"},{"key":"ref_54","unstructured":"Hoang, V.T., and Le, D.D. (2006). Wetland Classification System in Vietnam, Vietnam Environment Administration. CRES, Viet."},{"key":"ref_55","first-page":"486","article-title":"Composition and Productivity","volume":"53","author":"Stage","year":"2007","journal-title":"Soc. Am. For."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ghuffar, S. (2018). DEM generation from multi satellite Planetscope imagery. Remote Sens., 10.","DOI":"10.3390\/rs10091462"},{"key":"ref_57","first-page":"1689","article-title":"Digital Elevation Models of the Northern Gulf Coast: Procedures, Data sources and analysis","volume":"53","author":"Mussardo","year":"2019","journal-title":"Stat. F. Theor"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Perez, H., Tah, J.H.M., and Mosavi, A. (2019). Deep learning for detecting building defects using convolutional neural networks. Sensors (Switzerland), 19.","DOI":"10.20944\/preprints201908.0068.v1"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1109\/LGRS.2017.2722988","article-title":"Fusion of Deep Convolutional Neural Networks for Land Cover Classification of High-Resolution Imagery","volume":"14","author":"Scott","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zhang, P., Ke, Y., Zhang, Z., Wang, M., Li, P., and Zhang, S. (2018). Urban land use and land cover classification using novel deep learning models based on high spatial resolution satellite imagery. Sensors (Switzerland), 18.","DOI":"10.3390\/s18113717"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Liu, Z., Feng, R., Wang, L., Zhong, Y., and Cao, L. (2019). D-Resunet: Resunet and Dilated Convolution for High Resolution Satellite Imagery Road Extraction. Int. Geosci. Remote Sens. Symp., 3927\u20133930.","DOI":"10.1109\/IGARSS.2019.8898392"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Jakovljevic, G., Govedarica, M., and Alvarez-Taboada, F. (2020). A deep learning model for automatic plastic mapping using unmanned aerial vehicle (UAV) data. Remote Sens., 12.","DOI":"10.3390\/rs12091515"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"158223","DOI":"10.1109\/ACCESS.2019.2950371","article-title":"Deep Learning for Automatic Outlining Agricultural Parcels: Exploiting the Land Parcel Identification System","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2018.06.034","article-title":"An object-based convolutional neural network (OCNN) for urban land use classification","volume":"216","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_66","unstructured":"Iglovikov, V., Mushinskiy, S., and Osin, V. (2020, October 08). Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition. Available online: https:\/\/arxiv.org\/abs\/1706.06169."},{"key":"ref_67","unstructured":"Gulli, A., and Pal, S. (2017). Deep Learning with Keras\u2014Implement Neural Networks with Keras on Theano and TensorFlow, Packt Publishing Ltd."},{"key":"ref_68","first-page":"1","article-title":"Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification","volume":"8828","author":"Lapin","year":"2017","journal-title":"Pattern Anal. Mach. Intell."},{"key":"ref_69","unstructured":"Li, B., Liu, Y., and Wang, X. (February, January 27). Gradient Harmonized Single-Stage Detector. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ahuja, K. (2019, January 3\u20136). Estimating Kullback-Leibler Divergence Using Kernel Machines. Proceedings of the 2019 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/IEEECONF44664.2019.9049082"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Pasupa, K., Vatathanavaro, S., and Tungjitnob, S. (2020). Convolutional neural networks based focal loss for class imbalance problem: A case study of canine red blood cells morphology classification. J. Ambient Intell. Humaniz. Comput.","DOI":"10.1007\/s12652-020-01773-x"},{"key":"ref_73","first-page":"1","article-title":"A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons","volume":"5","year":"1948","journal-title":"K. Danske Vidensk. Selsk."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.neunet.2017.06.003","article-title":"Accelerating deep neural network training with inconsistent stochastic gradient descent","volume":"93","author":"Wang","year":"2017","journal-title":"Neural Networks"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., and Asari, V.K. (2019). A state-of-the-art survey on deep learning theory and architectures. Electronics, 8.","DOI":"10.3390\/electronics8030292"},{"key":"ref_76","unstructured":"Falbel, D., Allaire, J., Tang, Y., Van Der Bijl, W., and Keydana, S. (2020, October 08). R Interface to \u201cKeras\u201d. Available online: https:\/\/keras.rstudio.com."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_78","first-page":"15692","article-title":"Comparison of Random Forest and Support Vector Machine classifiers using UAV remote sensing imagery","volume":"19","author":"Piragnolo","year":"2017","journal-title":"Geophys. Res. Abstr. EGU Gen. Assem."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.agrformet.2016.11.002","article-title":"A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area","volume":"233","author":"Bui","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v015.i09","article-title":"Support Vector Algorithm in R","volume":"15","author":"Karatzoglou","year":"2006","journal-title":"J. Stat. Softw."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.jenvman.2019.04.095","article-title":"Ecosystem service value assessment of a natural reserve region for strengthening protection and conservation","volume":"244","author":"Sannigrahi","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Ge, W., Cheng, Q., Tang, Y., Jing, L., and Gao, C. (2018). Lithological classification using Sentinel-2A data in the Shibanjing ophiolite complex in Inner Mongolia, China. Remote Sens., 10.","DOI":"10.3390\/rs10040638"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.sjbs.2017.01.024","article-title":"Support vector machine-based open crop model (SBOCM): Case of rice production in China","volume":"24","author":"Su","year":"2017","journal-title":"Saudi J. Biol. Sci."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/s10346-016-0711-9","article-title":"Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization","volume":"14","author":"Tuan","year":"2017","journal-title":"Landslides"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3270\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:17:35Z","timestamp":1760177855000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3270"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,8]]},"references-count":84,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12193270"],"URL":"https:\/\/doi.org\/10.3390\/rs12193270","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,8]]}}}