{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:26:58Z","timestamp":1775539618769,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:00:00Z","timestamp":1670544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation","award":["42271392"],"award-info":[{"award-number":["42271392"]}]},{"name":"National Natural Science Foundation","award":["2021BID002"],"award-info":[{"award-number":["2021BID002"]}]},{"name":"National Natural Science Foundation","award":["2020AAA004"],"award-info":[{"award-number":["2020AAA004"]}]},{"name":"National Natural Science Foundation","award":["2020CFA005"],"award-info":[{"award-number":["2020CFA005"]}]},{"name":"National Natural Science Foundation","award":["2019(B)002"],"award-info":[{"award-number":["2019(B)002"]}]},{"name":"National Natural Science Foundation","award":["2020-2"],"award-info":[{"award-number":["2020-2"]}]},{"name":"National Natural Science Foundation","award":["NRMSSHR2022Y02"],"award-info":[{"award-number":["NRMSSHR2022Y02"]}]},{"name":"National Natural Science Foundation","award":["2019491611"],"award-info":[{"award-number":["2019491611"]}]},{"name":"National Key Research and Development Program of Hubei","award":["42271392"],"award-info":[{"award-number":["42271392"]}]},{"name":"National Key Research and Development Program of Hubei","award":["2021BID002"],"award-info":[{"award-number":["2021BID002"]}]},{"name":"National Key Research and Development Program of Hubei","award":["2020AAA004"],"award-info":[{"award-number":["2020AAA004"]}]},{"name":"National Key Research and Development Program of Hubei","award":["2020CFA005"],"award-info":[{"award-number":["2020CFA005"]}]},{"name":"National Key Research and Development Program of Hubei","award":["2019(B)002"],"award-info":[{"award-number":["2019(B)002"]}]},{"name":"National Key Research and Development Program of Hubei","award":["2020-2"],"award-info":[{"award-number":["2020-2"]}]},{"name":"National Key Research and Development Program of Hubei","award":["NRMSSHR2022Y02"],"award-info":[{"award-number":["NRMSSHR2022Y02"]}]},{"name":"National Key Research and Development Program of Hubei","award":["2019491611"],"award-info":[{"award-number":["2019491611"]}]},{"name":"Hubei Key Research and Development Program in China","award":["42271392"],"award-info":[{"award-number":["42271392"]}]},{"name":"Hubei Key Research and Development Program in China","award":["2021BID002"],"award-info":[{"award-number":["2021BID002"]}]},{"name":"Hubei Key Research and Development Program in China","award":["2020AAA004"],"award-info":[{"award-number":["2020AAA004"]}]},{"name":"Hubei Key Research and Development Program in China","award":["2020CFA005"],"award-info":[{"award-number":["2020CFA005"]}]},{"name":"Hubei Key Research and Development Program in China","award":["2019(B)002"],"award-info":[{"award-number":["2019(B)002"]}]},{"name":"Hubei Key Research and Development Program in China","award":["2020-2"],"award-info":[{"award-number":["2020-2"]}]},{"name":"Hubei Key Research and Development Program in China","award":["NRMSSHR2022Y02"],"award-info":[{"award-number":["NRMSSHR2022Y02"]}]},{"name":"Hubei Key Research and Development Program in China","award":["2019491611"],"award-info":[{"award-number":["2019491611"]}]},{"name":"Natural Science Foundation Key Projects of Hubei Province","award":["42271392"],"award-info":[{"award-number":["42271392"]}]},{"name":"Natural Science Foundation Key Projects of Hubei Province","award":["2021BID002"],"award-info":[{"award-number":["2021BID002"]}]},{"name":"Natural Science Foundation Key Projects of Hubei Province","award":["2020AAA004"],"award-info":[{"award-number":["2020AAA004"]}]},{"name":"Natural Science Foundation Key Projects of Hubei Province","award":["2020CFA005"],"award-info":[{"award-number":["2020CFA005"]}]},{"name":"Natural Science Foundation Key Projects of Hubei Province","award":["2019(B)002"],"award-info":[{"award-number":["2019(B)002"]}]},{"name":"Natural Science Foundation Key Projects of Hubei Province","award":["2020-2"],"award-info":[{"award-number":["2020-2"]}]},{"name":"Natural Science Foundation Key Projects of Hubei Province","award":["NRMSSHR2022Y02"],"award-info":[{"award-number":["NRMSSHR2022Y02"]}]},{"name":"Natural Science Foundation Key Projects of Hubei Province","award":["2019491611"],"award-info":[{"award-number":["2019491611"]}]},{"name":"Opening Foundation of Hubei Key Laboratory of Regional Development and Environmental Response","award":["42271392"],"award-info":[{"award-number":["42271392"]}]},{"name":"Opening Foundation of Hubei Key Laboratory of Regional Development and Environmental Response","award":["2021BID002"],"award-info":[{"award-number":["2021BID002"]}]},{"name":"Opening Foundation of Hubei Key Laboratory of Regional Development and Environmental Response","award":["2020AAA004"],"award-info":[{"award-number":["2020AAA004"]}]},{"name":"Opening Foundation of Hubei Key Laboratory of Regional Development and Environmental Response","award":["2020CFA005"],"award-info":[{"award-number":["2020CFA005"]}]},{"name":"Opening Foundation of Hubei Key Laboratory of Regional Development and Environmental Response","award":["2019(B)002"],"award-info":[{"award-number":["2019(B)002"]}]},{"name":"Opening Foundation of Hubei Key Laboratory of Regional Development and Environmental Response","award":["2020-2"],"award-info":[{"award-number":["2020-2"]}]},{"name":"Opening Foundation of Hubei Key Laboratory of Regional Development and Environmental Response","award":["NRMSSHR2022Y02"],"award-info":[{"award-number":["NRMSSHR2022Y02"]}]},{"name":"Opening Foundation of Hubei Key Laboratory of Regional Development and Environmental Response","award":["2019491611"],"award-info":[{"award-number":["2019491611"]}]},{"name":"Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring","award":["42271392"],"award-info":[{"award-number":["42271392"]}]},{"name":"Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring","award":["2021BID002"],"award-info":[{"award-number":["2021BID002"]}]},{"name":"Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring","award":["2020AAA004"],"award-info":[{"award-number":["2020AAA004"]}]},{"name":"Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring","award":["2020CFA005"],"award-info":[{"award-number":["2020CFA005"]}]},{"name":"Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring","award":["2019(B)002"],"award-info":[{"award-number":["2019(B)002"]}]},{"name":"Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring","award":["2020-2"],"award-info":[{"award-number":["2020-2"]}]},{"name":"Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring","award":["NRMSSHR2022Y02"],"award-info":[{"award-number":["NRMSSHR2022Y02"]}]},{"name":"Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring","award":["2019491611"],"award-info":[{"award-number":["2019491611"]}]},{"name":"Opening Foundation of Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources","award":["42271392"],"award-info":[{"award-number":["42271392"]}]},{"name":"Opening Foundation of Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources","award":["2021BID002"],"award-info":[{"award-number":["2021BID002"]}]},{"name":"Opening Foundation of Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources","award":["2020AAA004"],"award-info":[{"award-number":["2020AAA004"]}]},{"name":"Opening Foundation of Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources","award":["2020CFA005"],"award-info":[{"award-number":["2020CFA005"]}]},{"name":"Opening Foundation of Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources","award":["2019(B)002"],"award-info":[{"award-number":["2019(B)002"]}]},{"name":"Opening Foundation of Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources","award":["2020-2"],"award-info":[{"award-number":["2020-2"]}]},{"name":"Opening Foundation of Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources","award":["NRMSSHR2022Y02"],"award-info":[{"award-number":["NRMSSHR2022Y02"]}]},{"name":"Opening Foundation of Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources","award":["2019491611"],"award-info":[{"award-number":["2019491611"]}]},{"name":"Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering","award":["42271392"],"award-info":[{"award-number":["42271392"]}]},{"name":"Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering","award":["2021BID002"],"award-info":[{"award-number":["2021BID002"]}]},{"name":"Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering","award":["2020AAA004"],"award-info":[{"award-number":["2020AAA004"]}]},{"name":"Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering","award":["2020CFA005"],"award-info":[{"award-number":["2020CFA005"]}]},{"name":"Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering","award":["2019(B)002"],"award-info":[{"award-number":["2019(B)002"]}]},{"name":"Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering","award":["2020-2"],"award-info":[{"award-number":["2020-2"]}]},{"name":"Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering","award":["NRMSSHR2022Y02"],"award-info":[{"award-number":["NRMSSHR2022Y02"]}]},{"name":"Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering","award":["2019491611"],"award-info":[{"award-number":["2019491611"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Water quality grade is an intuitive element for people to understand the condition of water quality. However, in situ water quality grade measurements are often labor intensive, which makes measurement over large areas very costly and laborious. In recent years, numerous studies have demonstrated the effectiveness of remote sensing techniques in monitoring water quality. In order to automatically extract the water quality information, machine learning technologies have been widely applied in remote sensing data interoperation. In this study, Landsat-8 data and deep neural networks (DNN) were employed to identify the water quality grades of lakes in two cities, Wuhan and Huangshi, in the middle reach of the Yangtze River, central China. Additionally, linear support vector machine (L-SVM), random forest (RF), decision tree (DT), and multi-layer perceptron (MLP) were selected as comparative methods. The experimental results showed that DNN achieved the most promising performance compared to the other approaches. For the lakes in Wuhan, DNN gave water quality results with overall accuracy (OA) of 93.37% and Kappa of 0.9028. For the lakes in Huangshi, OA and kappa given by DNN were 96.39% and 0.951, respectively. The results show that the use of remote sensing images for water quality grade monitoring is effective. In the future, our method can be used for water quality monitoring of lakes in large areas at a low cost.<\/jats:p>","DOI":"10.3390\/rs14246238","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T06:14:00Z","timestamp":1670566440000},"page":"6238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Water Quality Grade Identification for Lakes in Middle Reaches of Yangtze River Using Landsat-8 Data with Deep Neural Networks (DNN) Model"],"prefix":"10.3390","volume":"14","author":[{"given":"Zeyang","family":"Wei","sequence":"first","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}]},{"given":"Lifei","family":"Wei","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"},{"name":"Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources, Changsha 410118, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9940-8273","authenticated-orcid":false,"given":"Hong","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Science, University of Reading, Reading RG6 6AB, UK"},{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China"}]},{"given":"Zhengxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}]},{"given":"Zhiwei","family":"Xiao","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}]},{"given":"Zhongqiang","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4878-8063","authenticated-orcid":false,"given":"Yujing","family":"Yang","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}]},{"given":"Guobin","family":"Xu","sequence":"additional","affiliation":[{"name":"Hubei Spatial Planning Research Institute, Wuhan 430061, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ma, X., Wang, L., Yang, H., Li, N., and Gong, C. (2020). Spatiotemporal Analysis of Water Quality Using Multivariate Statistical Techniques and the Water Quality Identification Index for the Qinhuai River Basin, East China. Water, 12.","DOI":"10.3390\/w12102764"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1126\/science.339.6116.141-b","article-title":"Sustaining China\u2019s Water Resources","volume":"339","author":"Yang","year":"2013","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1126\/science.337.6093.410-a","article-title":"Pollution in the Yangtze","volume":"337","author":"Yang","year":"2012","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1126\/science.347.6224.834-d","article-title":"Enforcement Key to China\u2019s Environment","volume":"347","author":"Yang","year":"2015","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Guo, Y., Yin, G., Zhang, X., Shi, Y., Hao, F., and Fu, Y. (2022). UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms\u2014A Case Study of the Zhanghe River, China. Remote Sens., 14.","DOI":"10.3390\/rs14143272"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Salazar, K., and Staub, G. (2021, January 11\u201316). Remote Sensing Based Analysis of Changes in Water Quality\u2014Case Study at Quintero Bay (Chile). Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554565"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102920","DOI":"10.1016\/j.jwpe.2022.102920","article-title":"Water Quality Classification Using Machine Learning Algorithms","volume":"48","author":"Nasir","year":"2022","journal-title":"J. Water Process Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"695","DOI":"10.14358\/PERS.69.6.695","article-title":"Remote Sensing Techniques to Assess Water Quality","volume":"69","author":"Ritchie","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Son, S., and Wang, M. (2020). Water Quality Properties Derived from VIIRS Measurements in the Great Lakes. Remote Sens., 12.","DOI":"10.3390\/rs12101605"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"111949","DOI":"10.1016\/j.rse.2020.111949","article-title":"Changes of Water Clarity in Large Lakes and Reservoirs across China Observed from Long-Term MODIS","volume":"247","author":"Wang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kupssinsk\u00fc, L.S., Guimar\u00e3es, T.T., and de Souza, E.M. (2020). A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning. Sensors, 20.","DOI":"10.3390\/s20072125"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111604","DOI":"10.1016\/j.rse.2019.111604","article-title":"Seamless Retrievals of Chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in Inland and Coastal Waters: A Machine-Learning Approach","volume":"240","author":"Pahlevan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111890","DOI":"10.1016\/j.rse.2020.111890","article-title":"Eutrophication Changes in Fifty Large Lakes on the Yangtze Plain of China Derived from MERIS and OLCI Observations","volume":"246","author":"Guan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"113815","DOI":"10.1016\/j.marpolbul.2022.113815","article-title":"Monitoring Multi-Temporal and Spatial Variations of Water Transparency in the Jiaozhou Bay Using GOCI Data","volume":"180","author":"Zhou","year":"2022","journal-title":"Mar. Pollut. Bull."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nazirova, K., Alferyeva, Y., Lavrova, O., Shur, Y., Soloviev, D., Bocharova, T., and Strochkov, A. (2021). Comparison of In Situ and Remote-Sensing Methods to Determine Turbidity and Concentration of Suspended Matter in the Estuary Zone of the Mzymta River, Black Sea. Remote Sens., 13.","DOI":"10.3390\/rs13010143"},{"key":"ref_16","unstructured":"(2002). Surface Water Environmental Quality Standards (Standard No. GB3838-2002). (In Chinese)."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1016\/S1001-0742(06)60032-6","article-title":"Entropy Method for Determination of Weight of Evaluating Indicators in Fuzzy Synthetic Evaluation for Water Quality Assessment","volume":"18","author":"Zou","year":"2006","journal-title":"J. Environ. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6783","DOI":"10.1021\/acs.est.1c00135","article-title":"Higher Fine Particle Fraction in Sediment Increased Phosphorus Flux to Estuary in Restored Yellow River Basin","volume":"55","author":"Wang","year":"2021","journal-title":"Environ. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"151992","DOI":"10.1016\/j.scitotenv.2021.151992","article-title":"Monitoring the Particulate Phosphorus Concentration of Inland Waters on the Yangtze Plain and Understanding Its Relationship with Driving Factors Based on OLCI Data","volume":"809","author":"Zeng","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.landurbplan.2009.10.002","article-title":"Spatial Impact of Urban Expansion on Surface Water Bodies\u2014A Case Study of Wuhan, China","volume":"94","author":"Du","year":"2010","journal-title":"Landsc. Urban Plan."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1016\/j.scitotenv.2016.09.213","article-title":"Microplastics Pollution in Inland Freshwaters of China: A Case Study in Urban Surface Waters of Wuhan, China","volume":"575","author":"Wang","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12326","DOI":"10.1021\/acs.est.1c02378","article-title":"Characterization of Dissolved Organic Matter and Its Derived Disinfection Byproduct Formation along the Yangtze River","volume":"55","author":"Fang","year":"2021","journal-title":"Environ. Sci. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"124644","DOI":"10.1016\/j.jclepro.2020.124644","article-title":"Spatiotemporal Water Quality Variations and Their Relationship with Hydrological Conditions in Dongting Lake after the Operation of the Three Gorges Dam, China","volume":"283","author":"Geng","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhou, Z.-H., and Feng, J. (2017, January 19\u201325). Deep Forest: Towards An Alternative to Deep Neural Networks. Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/497"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_27","first-page":"249","article-title":"Understanding the Difficulty of Training Deep Feedforward Neural Networks","volume":"9","author":"Glorot","year":"2010","journal-title":"J. Mach. Learn. Res.\u2014Proc. Track"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1600","DOI":"10.1109\/LGRS.2019.2905350","article-title":"A Multiple Feature Fully Convolutional Network for Road Extraction From High-Resolution Remote Sensing Image Over Mountainous Areas. IEEE Geosci","volume":"16","author":"Zhang","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.procs.2018.10.512","article-title":"Afiahayati Suitable CNN Weight Initialization and Activation Function for Javanese Vowels Classification","volume":"144","author":"Dewa","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"130893","DOI":"10.1109\/ACCESS.2019.2940653","article-title":"Making Deep Neural Networks Robust to Label Noise: Cross-Training with a Novel Loss Function","volume":"7","author":"Qin","year":"2019","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tang, M., Huang, Z., Yuan, Y., Wang, C., and Peng, Y. (2019). A Bounded Scheduling Method for Adaptive Gradient Methods. Appl. Sci., 9.","DOI":"10.3390\/app9173569"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"110929","DOI":"10.1109\/ACCESS.2020.3002590","article-title":"New Gradient-Weighted Adaptive Gradient Methods With Dynamic Constraints","volume":"8","author":"Liang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_33","first-page":"2121","article-title":"Adaptive Subgradient Methods for Online Learning and Stochastic Optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.neucom.2020.07.070","article-title":"AdaDB: An Adaptive Gradient Method with Data-Dependent Bound","volume":"419","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_35","unstructured":"Kingma, D.P., and Ba, J. (2017). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_36","unstructured":"Ghadimi, S., Lan, G., and Zhang, H. (2013). Mini-Batch Stochastic Approximation Methods for Nonconvex Stochastic Composite Optimization. arXiv."},{"key":"ref_37","unstructured":"Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T. (2016). One-Shot Learning with Memory-Augmented Neural Networks. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gilcher, M., Ruf, T., Emmerling, C., and Udelhoven, T. (2019). Remote Sensing Based Binary Classification of Maize. Dealing with Residual Autocorrelation in Sparse Sample Situations. Remote Sens., 11.","DOI":"10.3390\/rs11182172"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mugiraneza, T., Nascetti, A., and Ban, Y. (2019). WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices. Remote Sens., 11.","DOI":"10.3390\/rs11182128"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Phan, A., Ha, D.N., Man, C.D., Nguyen, T.T., Bui, H.Q., and Nguyen, T.T.N. (2019). Rapid Assessment of Flood Inundation and Damaged Rice Area in Red River Delta from Sentinel 1A Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11172034"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jiao, L., Sun, W., Yang, G., Ren, G., and Liu, Y. (2019). A Hierarchical Classification Framework of Satellite Multispectral\/Hyperspectral Images for Mapping Coastal Wetlands. Remote Sens., 11.","DOI":"10.3390\/rs11192238"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xiong, Y., Zhang, Q., and Chen, X. (2019). Large Scale Agricultural Plastic Mulch Detecting and Monitoring with Multi-Source Remote Sensing Data: A Case Study in Xinjiang, China. Remote Sens., 11.","DOI":"10.3390\/rs11182088"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mu\u00f1oz, D.F., Cissell, J.R., and Moftakhari, H. (2019). Adjusting Emergent Herbaceous Wetland Elevation with Object-Based Image Analysis, Random Forest and the 2016 NLCD. Remote Sens., 11.","DOI":"10.3390\/rs11202346"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Shirvani, Z., Abdi, O., and Buchroithner, M. (2019). A Synergetic Analysis of Sentinel-1 and -2 for Mapping Historical Landslides Using Object-Oriented Random Forest in the Hyrcanian Forests. Remote Sens., 11.","DOI":"10.3390\/rs11192300"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"243","DOI":"10.3390\/rs1030243","article-title":"An Automated Artificial Neural Network System for Land Use\/Land Cover Classification from Landsat TM Imagery","volume":"1","author":"Yuan","year":"2009","journal-title":"Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5019","DOI":"10.3390\/rs6065019","article-title":"Object-Based Image Classification of Summer Crops with Machine Learning Methods","volume":"6","year":"2014","journal-title":"Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Fu, G., Liu, C., Zhou, R., Sun, T., and Zhang, Q. (2017). Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network. Remote Sens., 9.","DOI":"10.3390\/rs9050498"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhu, K., Chen, Y., Ghamisi, P., Jia, X., and Benediktsson, J.A. (2019). Deep Convolutional Capsule Network for Hyperspectral Image Spectral and Spectral-Spatial Classification. Remote Sens., 11.","DOI":"10.3390\/rs11030223"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11269-018-2102-6","article-title":"Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach","volume":"33","author":"Miraki","year":"2018","journal-title":"Water Resour. Manag."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wei, L., Zhang, Y., Huang, C., Wang, Z., Huang, Q., Yin, F., Guo, Y., and Cao, L. (2020). Inland Lakes Mapping for Monitoring Water Quality Using a Detail\/Smoothing-Balanced Conditional Random Field Based on Landsat-8\/Levels Data. Sensors, 20.","DOI":"10.3390\/s20051345"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Pu, F., Ding, C., Chao, Z., Yu, Y., and Xu, X. (2019). Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11141674"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hashemi, S., Anthony, N., Tann, H., Bahar, R.I., and Reda, S. (2017, January 27\u201331). Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks. Proceedings of the Design, Automation Test in Europe Conference Exhibition (DATE), 2017, Lausanne, Switzerland.","DOI":"10.23919\/DATE.2017.7927224"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1038\/509535a","article-title":"China Must Continue the Momentum of Green Law","volume":"509","author":"Yang","year":"2014","journal-title":"Nature"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1038\/493163d","article-title":"China\u2019s New Leaders Offer Green Hope","volume":"493","author":"Yang","year":"2013","journal-title":"Nature"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wei, L., Guan, L., Qu, L., and Guo, D. (2020). Prediction of Sea Surface Temperature in the China Seas Based on Long Short-Term Memory Neural Networks. Remote Sens., 12.","DOI":"10.3390\/rs12172697"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, X., Huang, J., Feng, Q., and Yin, D. (2020). Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches. Remote Sens., 12.","DOI":"10.3390\/rs12111744"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chang, Y., and Luo, B. (2019). Bidirectional Convolutional LSTM Neural Network for Remote Sensing Image Super-Resolution. Remote Sens., 11.","DOI":"10.3390\/rs11202333"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ji, J., Jing, W., Chen, G., Lin, J., and Song, H. (2020). Multi-Label Remote Sensing Image Classification with Latent Semantic Dependencies. Remote Sens., 12.","DOI":"10.3390\/rs12071110"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6238\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:37:09Z","timestamp":1760146629000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6238"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,9]]},"references-count":58,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246238"],"URL":"https:\/\/doi.org\/10.3390\/rs14246238","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,9]]}}}