{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:51:51Z","timestamp":1775065911919,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T00:00:00Z","timestamp":1616976000000},"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":["41971356, 41701446, 42001340"],"award-info":[{"award-number":["41971356, 41701446, 42001340"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources","award":["KF-2020-05-011"],"award-info":[{"award-number":["KF-2020-05-011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mangroves play an important role in many aspects of ecosystem services. Mangroves should be accurately extracted from remote sensing imagery to dynamically map and monitor the mangrove distribution area. However, popular mangrove extraction methods, such as the object-oriented method, still have some defects for remote sensing imagery, such as being low-intelligence, time-consuming, and laborious. A pixel classification model inspired by deep learning technology was proposed to solve these problems. Three modules in the proposed model were designed to improve the model performance. A multiscale context embedding module was designed to extract multiscale context information. Location information was restored by the global attention module, and the boundary of the feature map was optimized by the boundary fitting unit. Remote sensing imagery and mangrove distribution ground truth labels obtained through visual interpretation were applied to build the dataset. Then, the dataset was used to train deep convolutional neural network (CNN) for extracting the mangrove. Finally, comparative experiments were conducted to prove the potential for mangrove extraction. We selected the Sentinel-2A remote sensing data acquired on 13 April 2018 in Hainan Dongzhaigang National Nature Reserve in China to conduct a group of experiments. After processing, the data exhibited 2093 \u00d7 2214 pixels, and a mangrove extraction dataset was generated. The dataset was made from Sentinel-2A satellite, which includes five original bands, namely R, G, B, NIR, and SWIR-1, and six multispectral indices, namely normalization difference vegetation index (NDVI), modified normalized difference water index (MNDWI), forest discrimination index (FDI), wetland forest index (WFI), mangrove discrimination index (MDI), and the first principal component (PCA1). The dataset has a total of 6400 images. Experimental results based on datasets show that the overall accuracy of the trained mangrove extraction network reaches 97.48%. Our method benefits from CNN and achieves a more accurate intersection and union ratio than other machine learning and pixel classification methods by analysis. The designed model global attention module, multiscale context embedding, and boundary fitting unit are helpful for mangrove extraction.<\/jats:p>","DOI":"10.3390\/rs13071292","type":"journal-article","created":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T04:29:14Z","timestamp":1616992154000},"page":"1292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4097-4814","authenticated-orcid":false,"given":"Mingqiang","family":"Guo","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China"}]},{"given":"Zhongyang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7421-4915","authenticated-orcid":false,"given":"Yongyang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Ying","family":"Huang","sequence":"additional","affiliation":[{"name":"Wuhan Zondy Cyber Technology Co., Ltd., Wuhan 430074, China"},{"name":"Wuhan Zondy Advanced Technology Institute Co., Ltd., Wuhan 430074, China"}]},{"given":"Chunfeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Environmental Studies, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1111\/j.1466-8238.2010.00584.x","article-title":"Status and distribution of mangrove forests of the world using earth observation satellite data","volume":"20","author":"Giri","year":"2011","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_2","first-page":"435","article-title":"Area, distribution and species composition of mangroves in China","volume":"12","author":"Liao","year":"2014","journal-title":"Wetl. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Giri, C. (2016). Observation and Monitoring of Mangrove Forests Using Remote Sensing: Opportunities and Challenges. Remote Sens., 8.","DOI":"10.3390\/rs8090783"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2739","DOI":"10.1080\/0143116031000066323","article-title":"High resolution mapping of tropical mangrove ecosystems using hyperspectral and radar remote sensing","volume":"24","author":"Held","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14360","DOI":"10.3390\/rs71114360","article-title":"Satellite images for monitoring mangrove cover changes in a fast growing economic region in southern Peninsular Malaysia","volume":"7","author":"Kanniah","year":"2015","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.ocecoaman.2011.12.004","article-title":"Mapping of mangrove forest land cover change along the Kenya coastline using Landsat imagery","volume":"83","author":"Kirui","year":"2013","journal-title":"Ocean Coast. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s41324-019-00268-y","article-title":"A review of the application of multispectral remote sensing in the study of mangrove ecosystems with special emphasis on image processing techniques","volume":"28","author":"Thakur","year":"2020","journal-title":"Spat. Inf. Res."},{"key":"ref_8","first-page":"30","article-title":"Next generation of global land cover characterization, mapping, and monitoring","volume":"25","author":"Giri","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"136","DOI":"10.3390\/rs8110954","article-title":"Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China","volume":"8","author":"Tian","year":"2016","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"799","DOI":"10.14358\/PERS.72.7.799","article-title":"Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery","volume":"72","author":"Yu","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wu, L., Xie, Z., and Chen, Z. (2018). Building extraction in very high resolution remote sensing imagery using deep learning and guided filters. Remote Sens., 10.","DOI":"10.3390\/rs10010144"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.04.031","article-title":"A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems","volume":"196","author":"Wang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1016\/j.proenv.2011.09.357","article-title":"Remote sensing of mangrove wetlands identification","volume":"10","author":"Fei","year":"2011","journal-title":"Procedia Environ. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ibrahim, N.A., Mustapha, M.A., Lihan, T., and Ghaffar, M.A. (2013). Determination of mangrove change in Matang Mangrove Forest using multi temporal satellite imageries. Proceedings of the AIP Conference Proceedings, American Institute of Physics.","DOI":"10.1063\/1.4858702"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional neural networks for large-scale remote-sensing image classification","volume":"55","author":"Maggiori","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bei, W., Guo, M., and Huang, Y. (2019). A Spatial Adaptive Algorithm Framework for Building Pattern Recognition Using Graph Convolutional Networks. Sensors, 19.","DOI":"10.3390\/s19245518"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, X., Luo, P., Loy, C.-C., and Tang, X. (2015, January 5\u20137). Semantic image segmentation via deep parsing network. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.162"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, Y., Xie, Z., Feng, Y., and Chen, Z. (2018). Road extraction from high-resolution remote sensing imagery using deep learning. Remote Sens., 10.","DOI":"10.3390\/rs10091461"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, M., Liu, H., Xu, Y., and Huang, Y. (2020). Building Extraction Based on U-Net with an Attention Block and Multiple Losses. Remote Sens., 12.","DOI":"10.3390\/rs12091400"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll, A.R.P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos, CA, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_25","unstructured":"Mnih, V., Heess, N., Graves, A., and Kavukcuoglu, K. (2014, January 8\u201313). Recurrent models of visual attention. Proceedings of the NIPS\u201914: Proceedings of the 27th International Conference on Neural Information Processing Systems, Cambridge, MA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_28","unstructured":"Li, H., Xiong, P., An, J., and Wang, L. (2018). Pyramid Attention Network for Semantic Segmentation. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhang, X., Yu, G., Luo, G., and Sun, J. (2017, January 21\u201326). Large Kernel Matters \u2014 Improve Semantic Segmentation by Global Convolutional Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu. HI. USA.","DOI":"10.1109\/CVPR.2017.189"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., and Torr, P.H. (2015, January 7\u201313). Conditional Random Fields as Recurrent Neural Networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.179"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, VA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1109\/JSTARS.2014.2333527","article-title":"Landsat-based estimation of mangrove forest loss and restoration in Guangxi province, China, influenced by human and natural factors","volume":"8","author":"Jia","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhen, J., Liao, J., and Shen, G. (2018). Mapping Mangrove Forests of Dongzhaigang Nature Reserve in China Using Landsat 8 and Radarsat-2 Polarimetric SAR Data. Sensors, 18.","DOI":"10.3390\/s18114012"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Splinter, K., Harley, M., and Turner, I. (2018). Remote Sensing Is Changing Our View of the Coast: Insights from 40 Years of Monitoring at Narrabeen-Collaroy, Australia. Remote Sens., 10.","DOI":"10.3390\/rs10111744"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Taureau, F., Robin, M., Proisy, C., Fromard, F., Imbert, D., and Debaine, F. (2019). Mapping the Mangrove Forest Canopy Using Spectral Unmixing of Very High Spatial Resolution Satellite Images. Remote Sens., 11.","DOI":"10.3390\/rs11030367"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., Wang, R., Sun, F., and Wu, X. (2018). Evaluating the Performance of Sentinel-2, Landsat 8 and Pl\u00e9iades-1 in Mapping Mangrove Extent and Species. Remote Sens., 10.","DOI":"10.3390\/rs10091468"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cao, J., Liu, K., Liu, L., Zhu, Y., Li, J., and He, Z. (2018). Identifying mangrove species using field close-range snapshot hyperspectral imaging and machine-learning techniques. Remote Sens., 10.","DOI":"10.3390\/rs10122047"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., and Cottrell, G. (2018, January 12\u201315). Understanding Convolution for Semantic Segmentation. Proceedings of the 2018 IEEE Winter Conference on Applications Of Computer Vision (WACV), Lake Tahoe, UV, USA.","DOI":"10.1109\/WACV.2018.00163"},{"key":"ref_41","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ballester, P., and Araujo, R.M. (2016, January 12\u201317). On the Performance of GoogLeNet and AlexNet Applied to Sketches. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10171"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.neucom.2016.11.023","article-title":"G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition","volume":"225","author":"Tang","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll, A.R.P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_46","unstructured":"Yu, F., and Koltun, V. (2016, January 2\u20134). Multi-Scale Context Aggregation by Dilated Convolutions. Proceedings of the International Conference on Learning Representations 2016, San Juan, Puerto Rico."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Springer International Publishing.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Guo, M., Song, Z., Han, C., Zhong, S., Lv, R., and Liu, Z. (2021). Mesh Denoising via Adaptive Consistent Neighborhood. Sensors, 21.","DOI":"10.3390\/s21020412"},{"key":"ref_49","first-page":"174","article-title":"A novel truncated nonconvex nonsmooth variational method for SAR image despeckling","volume":"12","author":"Guo","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1630","DOI":"10.1111\/tgis.12670","article-title":"A universal parallel scheduling approach to polyline and polygon vector data buffer analysis on conventional GIS platforms","volume":"24","author":"Guo","year":"2020","journal-title":"Trans. GIS"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1292\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:33:04Z","timestamp":1760362384000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1292"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,29]]},"references-count":50,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13071292"],"URL":"https:\/\/doi.org\/10.3390\/rs13071292","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,29]]}}}