{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T14:37:48Z","timestamp":1777300668122,"version":"3.51.4"},"reference-count":78,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,4,20]],"date-time":"2019-04-20T00:00:00Z","timestamp":1555718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801275"],"award-info":[{"award-number":["41801275"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Provincial Natural Science Foundation, China","award":["ZR2017MD007, ZR2018BD007"],"award-info":[{"award-number":["ZR2017MD007, ZR2018BD007"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["18CX05030A, 18CX02179A"],"award-info":[{"award-number":["18CX05030A, 18CX02179A"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postdoctoral Application and Research Projects of Qingdao","award":["BY20170204"],"award-info":[{"award-number":["BY20170204"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.<\/jats:p>","DOI":"10.3390\/rs11080952","type":"journal-article","created":{"date-parts":[[2019,4,22]],"date-time":"2019-04-22T11:02:53Z","timestamp":1555930973000},"page":"952","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles"],"prefix":"10.3390","volume":"11","author":[{"given":"Aizhu","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2641-2615","authenticated-orcid":false,"given":"Genyun","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9916-6382","authenticated-orcid":false,"given":"Xiuping","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Engineering and Information Technology, University of New South Wales at Canberra, Canberra ACT 2600, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6116-3194","authenticated-orcid":false,"given":"Jinchang","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5503-9090","authenticated-orcid":false,"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuming","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10661-006-9232-7","article-title":"The landscape pattern characteristics of coastal wetlands in Jiaozhou Bay under the impact of human activities","volume":"124","author":"Gu","year":"2007","journal-title":"Environ. Monit. Assess"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2015.04.009","article-title":"Super-resolution mapping of wetland inundation from remote sensing imagery based on integration of back-propagation neural network and genetic algorithm","volume":"164","author":"Li","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1080\/10106049.2015.1034194","article-title":"Study of coastal wetland classification based on decision rules using ALOS AVNIR-2 images and ancillary geospatial data","volume":"30","author":"Jiang","year":"2015","journal-title":"Geocarto Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/s12524-013-0357-1","article-title":"Mapping wetland areas using landsat-derived NDVI and LSWI: A case study of west Songnen Plain, Northeast China","volume":"42","author":"Dong","year":"2014","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cazals, C., Rapinel, S., Frison, P.-L., Bonis, A., Mercier, G., Mallet, C., Corgne, S., and Rudant, J.-P. (2016). Mapping and characterization of hydrological dynamics in a coastal marsh using high temporal resolution Sentinel-1A images. Remote Sens., 8.","DOI":"10.3390\/rs8070570"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"12575","DOI":"10.3390\/rs61212575","article-title":"The influence of polarimetric parameters and an object-based approach on land cover classification in coastal wetlands","volume":"6","author":"Chen","year":"2014","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2017.07.034","article-title":"A novel strategy for wetland area extraction using multispectral MODIS data","volume":"200","author":"Bansal","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chatziantoniou, A., Psomiadis, E., and Petropoulos, G. (2017). Co-orbital Sentinel 1 and 2 for LULC mapping with emphasis on wetlands in a mediterranean setting based on machine learning. Remote Sens., 9.","DOI":"10.3390\/rs9121259"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"22956","DOI":"10.3390\/s150922956","article-title":"Evaluating Sentinel-2 for lakeshore habitat mapping based on airborne hyperspectral data","volume":"15","author":"Stratoulias","year":"2015","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.rse.2012.05.015","article-title":"Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery","volume":"124","author":"Zhang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_11","first-page":"1","article-title":"A Comparison of change detection analyses using different band algebras for Baraila wetland with NASA\u2019s multi-temporal landsat dataset","volume":"7","author":"Ashraf","year":"2015","journal-title":"J. Geogr. Inf. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.gloplacha.2015.12.018","article-title":"Global coastal wetland change under sea-level rise and related stresses: The DIVA Wetland Change Model","volume":"139","author":"Spencer","year":"2016","journal-title":"Glob. Planet. Chang."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ecss.2014.07.009","article-title":"Surface elevation change and vegetation distribution dynamics in a subtropical coastal wetland: Implications for coastal wetland response to climate change","volume":"149","author":"Rogers","year":"2014","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1080\/10106049.2011.618846","article-title":"Fault-induced wetland loss at Matagorda, Texas, USA: Land cover changes from 1943 to 2008","volume":"26","author":"Cline","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2013.10.012","article-title":"Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping","volume":"87","author":"Demarchi","year":"2014","journal-title":"ISPRS J. Photogramm."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1029\/2012GL051276","article-title":"Changes in land surface water dynamics since the 1990s and relation to population pressure","volume":"39","author":"Prigent","year":"2012","journal-title":"Geophys. Res. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1109\/TGRS.2014.2358934","article-title":"A survey on spectral-spatial classification techniques based on attribute profiles","volume":"53","author":"Ghamisi","year":"2015","journal-title":"Ieee Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"12187","DOI":"10.3390\/rs61212187","article-title":"Improved wetland classification using eight-band high resolution satellite imagery and a hybrid approach","volume":"6","author":"Lane","year":"2014","journal-title":"Remote Sens."},{"key":"ref_19","unstructured":"Wu, Y., Wang, C., Yu, L., and Zhang, D. (2010, January 28\u201331). Using MRF approach to wetland classification of high spatial resolution remote sensing imagery: A case study in Xixi Westland National Park, Hangzhou, China. Proceedings of the Second Iita International Conference on Geoscience and Remote Sensing, Qingdao, China."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1109\/TGRS.2015.2496167","article-title":"Histogram-based attribute profiles for classification of very high resolution remote sensing images","volume":"54","author":"Demir","year":"2016","journal-title":"Ieee Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"991","DOI":"10.14358\/PERS.69.9.991","article-title":"Spatial metrics and image texture for mapping urban land use","volume":"69","author":"Herold","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2012.2197589","article-title":"Advances in spectral-spatial classification of hyperspectral images","volume":"101","author":"Fauvel","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1109\/JSTARS.2014.2298876","article-title":"Automatic framework for spectral\u2013spatial classification based on supervised feature extraction and morphological attribute profiles","volume":"7","author":"Ghamisi","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2018.02.005","article-title":"An unsupervised technique for optimal feature selection in attribute profiles for spectral-spatial classification of hyperspectral images","volume":"138","author":"Bhardwaj","year":"2018","journal-title":"ISPRS J. Photogramm."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2971","DOI":"10.1109\/JSTARS.2015.2432037","article-title":"Fusion of hyperspectral and LiDAR remote sensing data using multiple feature learning","volume":"8","author":"Khodadadzadeh","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1109\/LGRS.2012.2222340","article-title":"Change detection in VHR images based on morphological attribute profiles","volume":"10","author":"Falco","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Benediktsson, J.A., Bruzzone, L., Chanussot, J., Mura, M.D., Salembier, P., and Valero, S. (2011, January 6\u20138). Hierarchical analysis of remote sensing data: morphological attribute profiles and binary partition trees. Proceedings of the International Conference on Mathematical Morphology and its Applications to Image and Signal Processing, Verbania-Intra, Italy.","DOI":"10.1007\/978-3-642-21569-8_27"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5122","DOI":"10.1109\/TGRS.2013.2286953","article-title":"Remotely Sensed Image Classification Using Sparse Representations of Morphological Attribute Profiles","volume":"52","author":"Song","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Song, B., Li, J., Li, P., and Plaza, A. (2013, January 26\u201328). Decision fusion based on extended multi-attribute profiles for hyperspectral image classification. Proceedings of the Workshop on Hyperspectral Image & Signal Processing: Evolution in Remote Sensing, Gainesville, FL, USA.","DOI":"10.1109\/WHISPERS.2013.8080592"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.isprsjprs.2017.07.014","article-title":"A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification","volume":"140","author":"Zhang","year":"2018","journal-title":"ISPRS J. Photogramm."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yang, F., Guo, J., Tan, H., and Wang, J. (2017). Automated Extraction of Urban Water Bodies from ZY-3 Multi-Spectral Imagery. Water, 9.","DOI":"10.3390\/w9020144"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1109\/TGRS.2014.2367010","article-title":"A Novel Feature Selection Approach Based on FODPSO and SVM","volume":"53","author":"Ghamisi","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Berhane, T.M., Lane, C.R., Wu, Q., Anenkhonov, O.A., Chepinoga, V.V., Autrey, B.C., and Liu, H. (2018). Comparing Pixel- and Object-Based Approaches in Effectively Classifying Wetland-Dominated Landscapes. Remote Sens., 10.","DOI":"10.3390\/rs10010046"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jiang, W., He, G., Long, T., Ni, Y., Liu, H., Peng, Y., Lv, K., and Wang, G. (2018). Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images. Remote Sens., 10.","DOI":"10.3390\/rs10050755"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1016\/j.proenv.2011.09.368","article-title":"Wetland Landscape Classification Based on the BP Neural Network in DaLinor Lake Area","volume":"10","author":"Bao","year":"2011","journal-title":"Procedia Environ. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.knosys.2013.11.015","article-title":"Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting","volume":"56","author":"Ren","year":"2014","journal-title":"Knowl. Based Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.3390\/rs70201529","article-title":"Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking","volume":"7","author":"Taravat","year":"2015","journal-title":"Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1016\/j.future.2003.11.024","article-title":"Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification","volume":"20","author":"Liu","year":"2004","journal-title":"Future Gener. Comp. Sy"},{"key":"ref_41","unstructured":"Toshniwal, M. (2005, January 19\u201322). An optimized approach to application of neural networks to classification of multispectral, remote sensing data. Proceedings of the Networking, Sensing and Control Proceedings, Tucson, AZ, USA."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Zun-You, K., Ru, A., and Xiang-Juan, L. (2015, January 18\u201324). ANN Based High Spatial Resolution Remote Sensing Wetland Classification. Proceedings of the 2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), Guiyang, China.","DOI":"10.1109\/DCABES.2015.52"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.ins.2014.01.038","article-title":"Let a biogeography-based optimizer train your Multi-Layer Perceptron","volume":"269","author":"Mirjalili","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1007\/s10489-014-0645-7","article-title":"How effective is the Grey Wolf optimizer in training multi-layer perceptrons","volume":"43","author":"Mirjalili","year":"2015","journal-title":"Appl. Intell."},{"key":"ref_46","unstructured":"Xu, J., Yang, Y., and Zhang, R. (2015, January 15\u201317). Graduate enrollment prediction by an error back propagation algorithm based on the multi-experiential particle swarm optimization. Proceedings of the International Conference on Natural Computation, Zhangjiajie, China."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lian, C., Zeng, Z., Yao, W., Tang, H., and Chen, C.L. (2016). Landslide Displacement Prediction With Uncertainty Based on Neural Networks With Random Hidden Weights. IEEE Trans. Neural Netw. Learn. Syst.","DOI":"10.1109\/TNNLS.2015.2512283"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sheikhpour, S., Sabouri, M., and Zahiri, S.H. (2013, January 14\u201316). A hybrid gravitational search algorithm-genetic algorithm for neural network training. Proceedings of the Electr Eng, Mashhad, Iran.","DOI":"10.1109\/IranianCEE.2013.6599894"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1007\/s00500-013-1198-0","article-title":"Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis","volume":"18","author":"Beheshti","year":"2014","journal-title":"Soft Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s11721-012-0071-6","article-title":"Training feedforward neural networks with dynamic particle swarm optimisation","volume":"6","author":"Rakitianskaia","year":"2012","journal-title":"Swarm Intell."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Pratimsarangi, P., Sahu, A., and Panda, M. (2014). A Hybrid Differential Evolution and Back-Propagation Algorithm for Feedforward Neural Network Training. Int. J. Comput. Appl., 1\u20139.","DOI":"10.5120\/14641-2943"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5839","DOI":"10.1016\/j.eswa.2015.03.034","article-title":"Fuzzy logic in the gravitational search algorithm for the optimization of modular neural networks in pattern recognition","volume":"42","author":"Valdez","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"11125","DOI":"10.1016\/j.amc.2012.04.069","article-title":"Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm","volume":"218","author":"Mirjalili","year":"2012","journal-title":"Appl. Math. Comput."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","article-title":"GSA: A Gravitational Search Algorithm","volume":"179","author":"Rashedi","year":"2009","journal-title":"Inf. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.asoc.2014.07.016","article-title":"Convergence analysis and performance of an improved gravitational search algorithm","volume":"24","author":"Jiang","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.amc.2013.12.175","article-title":"Gravitational search algorithm combined with chaos for unconstrained numerical optimization","volume":"231","author":"Gao","year":"2014","journal-title":"Appl. Math. Comput."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhang, N., Li, C., Li, R., Lai, X., and Zhang, Y. (2016). A mixed-strategy based gravitational search algorithm for parameter identification of hydraulic turbine governing system. Know. Based Syst.","DOI":"10.1016\/j.knosys.2016.07.005"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.engappai.2015.12.016","article-title":"Parameter identification of a nonlinear model of hydraulic turbine governing system with an elastic water hammer based on a modified gravitational search algorithm","volume":"50","author":"Li","year":"2016","journal-title":"Eng. Appl. Artif. Intel."},{"key":"ref_59","unstructured":"Zhang, A., Sun, G., Ren, J., Li, X., Wang, Z., and Jia, X. (2016). A Dynamic Neighborhood Learning-Based Gravitational Search Algorithm. IEEE Trans. Cybern., 1\u201312."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1007\/s00521-014-1640-y","article-title":"Adaptive gbest-guided gravitational search algorithm","volume":"25","author":"Mirjalili","year":"2014","journal-title":"Neural Comput. Appl."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.knosys.2017.10.018","article-title":"A stability constrained adaptive alpha for gravitational search algorithm","volume":"139","author":"Sun","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhao, S., Qin, X., Zhao, N., and Liang, L. (2017). Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening. Remote Sens., 9.","DOI":"10.3390\/rs9060596"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1080\/17445647.2017.1372316","article-title":"Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia","volume":"13","author":"Clerici","year":"2018","journal-title":"J. Maps"},{"key":"ref_64","unstructured":"Amani, M., Salehi, B., Mahdavi, S., and Granger, J. (2013, January 26\u201328). Spectral analysis of wetlands in newfoundland using Sentinel 2A and Landsat 8 imagery. Proceedings of the ASPRS Conference, Baltimore, MA, USA."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Kaplan, G., and Avdan, U. (2017). Mapping and monitoring wetlands using Sentinel-2 satellite imagery. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., 271\u2013277.","DOI":"10.5194\/isprs-annals-IV-4-W4-271-2017"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Pham, M.-T., Lef\u00e8vre, S., and Merciol, F. (2018). Attribute profiles on derived textural features for highly textured optical image classification. IEEE Geosci. Remote Sens. Lett.","DOI":"10.1109\/LGRS.2018.2820817"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1109\/TPAMI.2007.28","article-title":"Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images","volume":"29","author":"Urbach","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rser.2013.12.008","article-title":"Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models","volume":"31","author":"Yadav","year":"2014","journal-title":"Renew. Sust. Energ. Rev."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.rse.2014.09.026","article-title":"Fractional snow cover estimation in complex alpine-forested environments using an artificial neural network","volume":"156","author":"Hirschboeck","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"4473","DOI":"10.3390\/rs70404473","article-title":"Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Sun, Y. (2008, January 21\u201322). Eco-environmental Quality and Ecological Restoration: A case study in wetland of the loushan river estuary, Jiaozhou Bay, Qingdao. Proceedings of the International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing, Shanghai, China.","DOI":"10.1109\/ETTandGRS.2008.176"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1007\/s13157-014-0542-1","article-title":"Spatio\u2013Temporal Dynamics of Wetland Landscape Patterns Based on Remote Sensing in Yellow River Delta, China","volume":"34","author":"Liu","year":"2014","journal-title":"Wetlands"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.ecohyd.2015.10.001","article-title":"An overview of ecohydrology of the Yellow River delta wetland","volume":"16","author":"Zhang","year":"2016","journal-title":"Ecohydrol. Hydrobiol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/22797254.2017.1297540","article-title":"Object-based water body extraction model using sentinel-2 satellite imagery","volume":"50","author":"Kaplan","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2011.06.028","article-title":"Capability of the sentinel 2 mission for tropical coral reef mapping and coral bleaching detection","volume":"120","author":"Hedley","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_76","first-page":"53","article-title":"Combinational shadow index for building shadow extraction in urban areas from sentinel-2a msi imagery","volume":"78","author":"Sun","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.rse.2005.10.014","article-title":"Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest)","volume":"99","author":"Lawrence","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Russell, I., and Markov, Z. (2017, January 8\u201311). An introduction to the Weka data mining system. Proceedings of the 2017 Sigcse Conference on Innovation & Technical Symposium on Computer Science Education (SIGCSE), Washington, DC, USA.","DOI":"10.1145\/3017680.3017821"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/8\/952\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:47:01Z","timestamp":1760186821000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/8\/952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,20]]},"references-count":78,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["rs11080952"],"URL":"https:\/\/doi.org\/10.3390\/rs11080952","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,20]]}}}