{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T17:26:39Z","timestamp":1772213199289,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T00:00:00Z","timestamp":1628208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Driven by the urgent demand for flood monitoring, water resource management and environmental protection, water-body detection in remote sensing imagery has attracted increasing research attention. Deep semantic segmentation networks (DSSNs) have gradually become the mainstream technology used for remote sensing image water-body detection, but two vital problems remain. One problem is that the traditional structure of DSSNs does not consider multiscale and multishape characteristics of water bodies. Another problem is that a large amount of unlabeled data is not fully utilized during the training process, but the unlabeled data often contain meaningful supervision information. In this paper, we propose a novel multiscale residual network (MSResNet) that uses self-supervised learning (SSL) for water-body detection. More specifically, our well-designed MSResNet distinguishes water bodies with different scales and shapes and helps retain the detailed boundaries of water bodies. In addition, the optimization of MSResNet with our SSL strategy can improve the stability and universality of the method, and the presented SSL approach can be flexibly extended to practical applications. Extensive experiments on two publicly open datasets, including the 2020 Gaofen Challenge water-body segmentation dataset and the GID dataset, demonstrate that our MSResNet can obviously outperform state-of-the-art deep learning backbones and that our SSL strategy can further improve the water-body detection performance.<\/jats:p>","DOI":"10.3390\/rs13163122","type":"journal-article","created":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T21:35:40Z","timestamp":1628458540000},"page":"3122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["MSResNet: Multiscale Residual Network via Self-Supervised Learning for Water-Body Detection in Remote Sensing Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8079-029X","authenticated-orcid":false,"given":"Bo","family":"Dang","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8203-1246","authenticated-orcid":false,"given":"Yansheng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.inffus.2020.10.008","article-title":"Image retrieval from remote sensing big data: A survey","volume":"67","author":"Li","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/JPROC.2016.2598228","article-title":"Big data for remote sensing: Challenges and opportunities","volume":"104","author":"Chi","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.future.2014.10.029","article-title":"Remote sensing big data computing: Challenges and opportunities","volume":"51","author":"Ma","year":"2015","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1029\/2018RG000598","article-title":"Detecting, extracting, and monitoring surface water from space using optical sensors: A review","volume":"56","author":"Huang","year":"2018","journal-title":"Rev. Geophys."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, P., Xing, J., Li, Z., Xing, X., and Yuan, Z. (2020). A multi-scale deep neural network for water detection from SAR images in the mountainous areas. Remote Sens., 12.","DOI":"10.3390\/rs12193205"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TGRS.2020.2999405","article-title":"Water body detection in high-resolution SAR images with cascaded fully-convolutional network and variable focal loss","volume":"59","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Balajee, J., and Durai, M.A.S. (2021). Detection of water availability in SAR images using deep learning architecture. Int. J. Syst. Assur. Eng. Manag., 1\u201310.","DOI":"10.1007\/s13198-021-01152-5"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","first-page":"1461","article-title":"Water body detection and delineation with Landsat TM data. Photogrammetric engineering and remote sensing","volume":"66","author":"Frazier","year":"2000","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_10","unstructured":"Lv, W., Yu, Q., and Yu, W. (2010, January 24\u201328). Water extraction in SAR images using GLCM and support vector machine. Proceedings of the IEEE 10th International Conference on Signal Processing, Beijing, China."},{"key":"ref_11","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 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., and Culurciello, E. (2017, January 10\u201313). LinkNet: Exploiting encoder representations for efficient semantic segmentation. Proceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"ref_13","unstructured":"Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., and Wang, J. (2019). High-resolution representations for labeling pixels and regions. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 9). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1109\/LGRS.2018.2879492","article-title":"Water Body Extraction from Very High-Resolution Remote Sensing Imagery Using Deep U-Net and a Superpixel-Based Conditional Random Field Model","volume":"16","author":"Feng","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guo, H., He, G., Jiang, W., Yin, R., Yan, L., and Leng, W. (2020). A Multi-Scale Water Extraction Convolutional Neural Network (MWEN) Method for GaoFen-1 Remote Sensing Images. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9040189"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1109\/LGRS.2019.2926412","article-title":"Multiscale Refinement Network for Water-Body Segmentation in High-Resolution Satellite Imagery","volume":"17","author":"Duan","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., and Efros, A.A. (2016, January 27\u201330). Context Encoders: Feature Learning by Inpainting. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., and Efros, A.A. (2016, January 11\u201314). Colorful image colorization. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"ref_20","unstructured":"Gidaris, S., Singh, P., and Komodakis, N. (2018). Unsupervised representation learning by predicting image rotations. arXiv."},{"key":"ref_21","first-page":"766","article-title":"Discriminative unsupervised feature learning with convolutional neural networks","volume":"27","author":"Dosovitskiy","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jiang, H., Larsson, G., Shakhnarovich, M.M.G., and Learned-Miller, E. (2018, January 8\u201314). Self-supervised relative depth learning for urban scene understanding. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_2"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, Y., Paluri, M., Rehg, J.M., and Doll\u00e1r, P. (2016, January 27\u201330). Unsupervised learning of edges. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.179"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jing, L., and Tian, Y. (2020). Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey. IEEE Trans. Pattern Anal. Mach. Intell., 1.","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"ref_25","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 11\u201313). A simple framework for contrastive learning of visual representations. Proceedings of the International Conference on Machine Learning, Shangri-La, China."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5398","DOI":"10.1109\/JSTARS.2020.3021098","article-title":"BAS44Net: Boundary-aware semi-supervised semantic segmentation network for very high resolution remote sensing images","volume":"13","author":"Sun","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","unstructured":"Tong, X.-Y., Xia, G.-S., Lu, Q., Shen, H., Li, S., You, S., and Zhang, L. (2018). Learning transferable deep models for land-use classification with high-resolution remote sensing images. arXiv."},{"key":"ref_29","first-page":"589","article-title":"A study on information extraction of water body with the modified normalized difference water index (MNDWI)","volume":"5","year":"2005","journal-title":"J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.rse.2013.08.029","article-title":"Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery","volume":"140","author":"Feyisa","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.rse.2015.12.055","article-title":"Comparing Landsat water index methods for automated water classification in eastern Australia","volume":"175","author":"Fisher","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.1080\/01431161.2016.1278284","article-title":"Waterbody information extraction from remote-sensing images after disasters based on spectral information and characteristic knowledge","volume":"38","author":"Zhao","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"V\u00e9lez-Nicol\u00e1s, M., Garc\u00eda-L\u00f3pez, S., Barbero, L., Ruiz-Ortiz, V., and S\u00e1nchez-Bell\u00f3n, \u00c1. (2021). Applications of unmanned aerial systems (UASs) in hydrology: A review. Remote Sens., 13.","DOI":"10.3390\/rs13071359"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jakovljevi\u0107, G., and Govedarica, M. (2019). Water Body Extraction and Flood Risk Assessment Using Lidar and Open Data. Climate Change Management, Springer Science and Business Media LLC.","DOI":"10.1007\/978-3-030-03383-5_7"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Morsy, S., Shaker, A., and El-Rabbany, A. (2018). Using Multispectral airborne lidar data for land\/water discrimination: A case study at Lake Ontario, Canada. Appl. Sci., 8.","DOI":"10.3390\/app8030349"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1007\/s11269-017-1568-y","article-title":"Floodplain mapping through support vector machine and optical\/infrared images from Landsat 8 OLI\/TIRS sensors: Case study from Varanasi","volume":"31","author":"Nandi","year":"2017","journal-title":"Water Resour. Manag."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_38","unstructured":"Hong, Y., Pan, H., Sun, W., and Jia, Y. (2021). Deep dual-resolution networks for real-time and accurate semantic segmentation of road scenes. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.1109\/TCYB.2020.2989241","article-title":"Error-Tolerant Deep Learning for Remote Sensing Image Scene Classification","volume":"51","author":"Li","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1109\/TBDATA.2019.2948924","article-title":"Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation","volume":"6","author":"Tong","year":"2020","journal-title":"IEEE Trans. Big Data"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2021.02.009","article-title":"Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation","volume":"175","author":"Li","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ming, Q., Miao, L., Zhou, Z., and Dong, Y. (2021). Cfc-net: A critical feature capturing network for arbitrary-oriented object detection in remote sensing images. arXiv.","DOI":"10.1109\/TGRS.2021.3095186"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"112045","DOI":"10.1016\/j.rse.2020.112045","article-title":"Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning","volume":"250","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1142\/S1793351X20400036","article-title":"Improved semantic segmentation of water bodies and land in SAR images using generative adversarial networks","volume":"14","author":"Pai","year":"2020","journal-title":"Int. J. Semant. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, L., Yan, Z., Shen, Q., Cheng, G., Gao, L., and Zhang, B. (2019). Water body extraction from very high spatial resolution remote sensing data based on fully convolutional networks. Remote Sens., 11.","DOI":"10.3390\/rs11101162"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Song, S., Liu, J., Liu, Y., Feng, G., Han, H., Yao, Y., and Du, M. (2020). Intelligent object recognition of urban water bodies based on deep learning for multi-source and multi-temporal high spatial resolution remote sensing imagery. Sensors, 20.","DOI":"10.3390\/s20020397"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1750001","DOI":"10.1142\/S1469026817500018","article-title":"Convolutional neural networks for water body extraction from landsat imagery","volume":"16","author":"Yu","year":"2017","journal-title":"Int. J. Comput. Intell. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1109\/LGRS.2018.2794545","article-title":"Automatic water-body segmentation from high-resolution satellite images via deep networks","volume":"15","author":"Miao","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"125092","DOI":"10.1016\/j.jhydrol.2020.125092","article-title":"A novel water body extraction neural network (WBE-NN) for optical high-resolution multispectral imagery","volume":"588","author":"Chen","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"155787","DOI":"10.1109\/ACCESS.2019.2949635","article-title":"Multiscale features supported Deeplabv3+ optimization scheme for accurate water semantic segmentation","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lu, M., Ji, S., Yu, H., and Nie, C. (2021). Rich CNN Features for water-body segmentation from very high resolution aerial and satellite imagery. Remote Sens., 13.","DOI":"10.3390\/rs13101912"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wu, Y., Han, P., and Zheng, Z. (2021). Instant water body variation detection via analysis on remote sensing imagery. J. Real Time Image Process., 1\u201314.","DOI":"10.1007\/s11554-020-01062-y"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Fu, K., Lu, W., Diao, W., Yan, M., Sun, H., Zhang, Y., and Sun, X. (2018). WSF-NET: Weakly Supervised feature-fusion network for binary segmentation in remote sensing image. Remote Sens., 10.","DOI":"10.3390\/rs10121970"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 1\u201326). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_55","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013). Network in network. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhou, L., Zhang, C., and Wu, M. (2018, January 18\u201322). D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00034"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Shi, H., Wang, H., Jin, Y., Zhao, L., and Liu, C. (2019, January 4\u20139). Automated heartbeat classification based on convolutional neural network with multiple kernel sizes. Proceedings of the 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), Newark, CA, USA.","DOI":"10.1109\/BigDataService.2019.00055"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., and Yoo, Y. (November, January 27). CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., and Garcia-Rodriguez, J. (2017). A review on deep learning techniques applied to semantic segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_61","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3122\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:42:04Z","timestamp":1760164924000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3122"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,6]]},"references-count":61,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163122"],"URL":"https:\/\/doi.org\/10.3390\/rs13163122","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,6]]}}}