{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T23:28:42Z","timestamp":1776382122348,"version":"3.51.2"},"reference-count":51,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,4,28]],"date-time":"2019-04-28T00:00:00Z","timestamp":1556409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2018M641529"],"award-info":[{"award-number":["2018M641529"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Land and Resources Industry Public Welfare Projects","award":["201511010-06"],"award-info":[{"award-number":["201511010-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Coastal land cover classification is a significant yet challenging task in remote sensing because of the complex and fragmented nature of coastal landscapes. However, availability of multitemporal and multisensor remote sensing data provides opportunities to improve classification accuracy. Meanwhile, rapid development of deep learning has achieved astonishing results in computer vision tasks and has also been a popular topic in the field of remote sensing. Nevertheless, designing an effective and concise deep learning model for coastal land cover classification remains problematic. To tackle this issue, we propose a multibranch convolutional neural network (MBCNN) for the fusion of multitemporal and multisensor Sentinel data to improve coastal land cover classification accuracy. The proposed model leverages a series of deformable convolutional neural networks to extract representative features from a single-source dataset. Extracted features are aggregated through an adaptive feature fusion module to predict final land cover categories. Experimental results indicate that the proposed MBCNN shows good performance, with an overall accuracy of 93.78% and a Kappa coefficient of 0.9297. Inclusion of multitemporal data improves accuracy by an average of 6.85%, while multisensor data contributes to 3.24% of accuracy increase. Additionally, the featured fusion module in this study also increases accuracy by about 2% when compared with the feature-stacking method. Results demonstrate that the proposed method can effectively mine and fuse multitemporal and multisource Sentinel data, which improves coastal land cover classification accuracy.<\/jats:p>","DOI":"10.3390\/rs11091006","type":"journal-article","created":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T02:57:32Z","timestamp":1556506652000},"page":"1006","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["Integrating Multitemporal Sentinel-1\/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta"],"prefix":"10.3390","volume":"11","author":[{"given":"Quanlong","family":"Feng","sequence":"first","affiliation":[{"name":"College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China"},{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}]},{"given":"Jianyu","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}]},{"given":"Dehai","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}]},{"given":"Jiantao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}]},{"given":"Hao","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}]},{"given":"Batsaikhan","family":"Bayartungalag","sequence":"additional","affiliation":[{"name":"Research Center for Ecology and Sustainable Development, Mongolian University of Science and Technology, Ulaanbaatar 14191, Mongolia"}]},{"given":"Baoguo","family":"Li","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8516","DOI":"10.3390\/rs70708516","article-title":"Remote Sensing of River Delta Inundation: Exploiting the Potential of Coarse Spatial Resolution, Temporally-Dense MODIS Time Series","volume":"7","author":"Kuenzer","year":"2015","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1002\/ldr.2339","article-title":"Analysis of Land use and Land Cover Changes in the Coastal Area of Bangladesh using Landsat Imagery","volume":"27","author":"Islam","year":"2016","journal-title":"Land Degrad. Develop."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s11273-014-9342-x","article-title":"Mapping agricultural wetlands in the Sacramento Valley, USA with satellite remote sensing","volume":"23","author":"Torbick","year":"2015","journal-title":"Wetlands Ecol. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5809","DOI":"10.1080\/01431160801958405","article-title":"Radar detection of wetland ecosystems: a review","volume":"29","author":"Henderson","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1080\/15481603.2017.1419602","article-title":"Remote sensing for wetland classification: a comprehensive review","volume":"55","author":"Mahdavi","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1016\/j.rse.2009.10.009","article-title":"Wetland monitoring using classification trees and SPOT-5 seasonal time series","volume":"114","author":"Davranche","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10661-015-4667-3","article-title":"Rule-based land use\/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China","volume":"187","author":"Yang","year":"2015","journal-title":"Environ. Monit. Assess."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14834","DOI":"10.3390\/su71114834","article-title":"Monitoring Cropland Dynamics of the Yellow River Delta based on Multi-Temporal Landsat Imagery over 1986 to 2015","volume":"7","author":"Feng","year":"2015","journal-title":"Sustainability"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s13157-010-0135-6","article-title":"Use of multi-sensor data to identify and map tropical coastal wetlands in the Amazon of Northern Brazil","volume":"31","author":"Rodrigues","year":"2011","journal-title":"Wetlands."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2014.04.010","article-title":"Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data","volume":"149","author":"Beijma","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.3390\/rs5073212","article-title":"Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota","volume":"5","author":"Corcoran","year":"2013","journal-title":"Remote Sens."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1080\/01431161.2017.1410295","article-title":"Wetland classification using Radarsat-2 SAR quad-polarization and Landsat-8 OLI spectral response data: a case study in the Hudson Bay Lowlands Ecoregion","volume":"39","author":"Franklin","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","unstructured":"Hird, J.N., DeLancey, E.R., McDermid, G.J., and Kariyeva, J. (2019). Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping. Remote Sens., 11."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Homayouni, S., and Gill, E. (2017). The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Remote Sens., 9.","DOI":"10.3390\/rs11010043"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.rse.2018.07.006","article-title":"Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery","volume":"216","author":"Erinjery","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hajj, M.E., Baghdadi, N., Zribi, M., and Bazzi, H. (2017). Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas. Remote Sens., 9.","DOI":"10.3390\/rs9121292"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tricht, K.V., Gobin, A., Gilliams, S., and Piccard, I. (2018). Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium. Remote Sens., 10.","DOI":"10.3390\/rs10101642"},{"key":"ref_19","unstructured":"Muller-Wilm, U. (2019, April 27). Sentinel-2 MSI \u2013 Level-2A Prototype Processor Installation and User Manual. Available online: http:\/\/step.esa.int\/thirdparties\/sen2cor\/2.2.1\/S2PAD-VEGA-SUM-0001-2.2.pdf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_21","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Proc. Adv. Neural Inf. Process. Syst., 1097\u20131105."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bulat, A., and Tzimiropoulos, G. (2017). Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources. Proc. IEEE Int. Conf. Comput. Vis., 3706\u20133714.","DOI":"10.1109\/ICCV.2017.400"},{"key":"ref_23","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2019, April 27). Deformable Convolutional Networks. Arxiv 2017 [1703.06211]. Available online: https:\/\/arxiv.org\/pdf\/1703.06211.pdf."},{"key":"ref_24","unstructured":"Jin, Q., Meng, Z., Pham, T.D., Chen, Q., Wei, L., and Su, R. (2019, April 27). DUNet: A Deformable Network for Retinal Vessel Segmentation. Arxiv 2018 [1811.01206]. Available online: https:\/\/arxiv.org\/pdf\/1811.01206.pdf."},{"key":"ref_25","unstructured":"Hu, J., Shen, L., and Sun, G. (2019, April 27). Squeeze-and-Excitation Networks. Arxiv 2017 [1709.01507]. Available online: https:\/\/arxiv.org\/pdf\/1709.01507.pdf."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017). Focal loss for dense object detection. Proc. IEEE Int. Conf. Comput. Vis., 2999\u20133007.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. M."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pan, X., Gao, L., Marinoni, A., Zhang, B., Yang, F., and Gamba, P. (2018). Semantic Labeling of High Resolution Aerial Imagery and LiDAR Data with Fine Segmentation Network. Remote Sens., 10.","DOI":"10.3390\/rs10050743"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Feng, Q., Zhu, D., Yang, J., and Li, B. (2019). Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping based on a Modified Two-Branch Convolutional Neural Network. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8010028"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3011","DOI":"10.1109\/JSTARS.2016.2634863","article-title":"Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network","volume":"10","author":"Ghamisi","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1109\/LGRS.2018.2799232","article-title":"Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN","volume":"15","author":"Hughes","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/TGRS.2017.2756851","article-title":"Multisource Remote Sensing Data Classification Based on Convolutional Neural Network","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3030","DOI":"10.1109\/JSTARS.2018.2846178","article-title":"Deep Convolutional Neural Network for Complex Wetland Classification Using Optical Remote Sensing Imagery","volume":"11","author":"Rezaee","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., and K\u00f6rner, M. (2018). Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7040129"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ji, S., Zhang, C., Xu, A., Shi, Y., and Duan, Y. (2018). 3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10010075"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Scarpa, G., Gargiulo, M., Mazza, A., and Gaetano, R. (2018). A CNN-Based Fusion Method for Feature Extraction from Sentinel Data. Remote Sens., 10.","DOI":"10.3390\/rs10020236"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mahdianpari, M., Salehi, B., Rezaee, M., Mohammadimanesh, F., and Zhang, Y. (2018). Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10071119"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yang, J., Ren, G., Ma, Y., and Fan, Y. (2016). Coastal wetland classification based on high resolution SAR and optical image fusion. Proc. IEEE Int. Conf. Comput. Vis., 886\u2013889.","DOI":"10.1109\/IGARSS.2016.7729224"},{"key":"ref_40","first-page":"53","article-title":"Monitoring land cover dynamics in the Yellow River Delta from 1995 to 2010 based on Landsat 5 TM","volume":"44","author":"Ottinger","year":"2013","journal-title":"Appl. Geol."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1080\/01431161.2016.1165888","article-title":"Land-cover classification of the Yellow River Delta wetland based on multiple end-member spectral mixture analysis and a Random Forest classifier","volume":"37","author":"Liu","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2019, April 27). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Arxiv 2015 [1502.01852]. Available online: https:\/\/arxiv.org\/pdf\/1502.01852.pdf.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_44","unstructured":"Kingma, D.P., and Ba, J. (2019, April 27). Adam: A Method for Stochastic Optimization. Arxiv 2014 [1412.6980]. Available online: https:\/\/arxiv.org\/pdf\/1412.6980.pdf."},{"key":"ref_45","unstructured":"(2018, November 17). TensorFlow. Available online: https:\/\/tensorflow.google.cn\/."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1023\/A:1012450327387","article-title":"Choosing Multiple Parameters for Support Vector Machines","volume":"46","author":"Chapelle","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eja.2018.10.008","article-title":"Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST\u2013PROSAIL model","volume":"102","author":"Huang","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.agrformet.2015.10.013","article-title":"Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to estimate regional winter wheat yield","volume":"216","author":"Huang","year":"2016","journal-title":"Agr. Forest Meteorol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.agrformet.2015.02.001","article-title":"Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model","volume":"204","author":"Huang","year":"2015","journal-title":"Agr. Forest Meteorol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4060","DOI":"10.1109\/JSTARS.2015.2403135","article-title":"Jointly assimilating MODIS LAI and ET products into the SWAP model for winter wheat yield estimation","volume":"8","author":"Huang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1006\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:47:40Z","timestamp":1760186860000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1006"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,28]]},"references-count":51,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["rs11091006"],"URL":"https:\/\/doi.org\/10.3390\/rs11091006","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,28]]}}}