{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T03:49:44Z","timestamp":1791085784090,"version":"4.1.0"},"reference-count":36,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T00:00:00Z","timestamp":1620864000000},"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>Extracting water-bodies accurately is a great challenge from very high resolution (VHR) remote sensing imagery. The boundaries of a water body are commonly hard to identify due to the complex spectral mixtures caused by aquatic vegetation, distinct lake\/river colors, silts near the bank, shadows from the surrounding tall plants, and so on. The diversity and semantic information of features need to be increased for a better extraction of water-bodies from VHR remote sensing images. In this paper, we address these problems by designing a novel multi-feature extraction and combination module. This module consists of three feature extraction sub-modules based on spatial and channel correlations in feature maps at each scale, which extract the complete target information from the local space, larger space, and between-channel relationship to achieve a rich feature representation. Simultaneously, to better predict the fine contours of water-bodies, we adopt a multi-scale prediction fusion module. Besides, to solve the semantic inconsistency of feature fusion between the encoding stage and the decoding stage, we apply an encoder-decoder semantic feature fusion module to promote fusion effects. We carry out extensive experiments in VHR aerial and satellite imagery respectively. The result shows that our method achieves state-of-the-art segmentation performance, surpassing the classic and recent methods. Moreover, our proposed method is robust in challenging water-body extraction scenarios.<\/jats:p>","DOI":"10.3390\/rs13101912","type":"journal-article","created":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T03:28:36Z","timestamp":1620962916000},"page":"1912","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["Rich CNN Features for Water-Body Segmentation from Very High Resolution Aerial and Satellite Imagery"],"prefix":"10.3390","volume":"13","author":[{"given":"Zhili","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6850-581X","authenticated-orcid":false,"given":"Meng","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Physical Geography, Utrecht University, 3584 CB Utrecht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3088-1481","authenticated-orcid":false,"given":"Shunping","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huafen","family":"Yu","sequence":"additional","affiliation":[{"name":"Zhejiang Academy of Surveying and Mapping, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenhui","family":"Nie","sequence":"additional","affiliation":[{"name":"Zhejiang Academy of Surveying and Mapping, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3221","DOI":"10.1007\/s11269-009-9431-4","article-title":"Water quality monitoring and modeling in Lake Kastoria, using GIS. Assessment and management of pollution sources","volume":"23","author":"Mantzafleri","year":"2009","journal-title":"Water Resour. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"666","DOI":"10.2166\/wst.2012.227","article-title":"Assessment of health hazard associated with nitrogen compounds in water","volume":"66","year":"2012","journal-title":"Water Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2619","DOI":"10.1016\/j.proenv.2011.09.407","article-title":"Water body extraction methods study based on RS and GIS","volume":"10","author":"Haibo","year":"2011","journal-title":"Procedia Environ. Sci."},{"key":"ref_4","first-page":"1461","article-title":"Water body detection and delineation with Landsat TM data","volume":"66","author":"Frazier","year":"2000","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1016\/j.aqpro.2015.02.095","article-title":"Assessment of surface water Dynamicsin Bangalore using WRI, NDWI, MNDWI, supervised classification and KT transformation","volume":"4","author":"Gautam","year":"2015","journal-title":"Aquat. Procedia"},{"key":"ref_6","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_7","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_8","first-page":"640","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Long","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). 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_10","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., and Reid, I. (2017, January 22\u201325). Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yu, Z., Feng, C., Liu, M.-Y., and Ramalingam, S. (2017, January 21\u201326). Casenet: Deep category-aware semantic edge detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.191"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bertasius, G., Shi, J., and Torresani, L. (2015, January 8\u201310). Deepedge: A multi-scale bifurcated deep network for top-down contour detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299067"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xie, S., and Tu, Z. (2015, January 13\u201316). Holistically-nested edge detection. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.164"},{"key":"ref_14","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_15","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_16","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_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":"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_19","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_20","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, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_22","unstructured":"Zhang, H., Wu, C., Zhang, Z., Zhu, Y., Zhang, Z., Lin, H., Sun, Y., He, T., Mueller, J., and Manmatha, R. (2020). Resnest: Split-attention networks. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., and Jagersand, M. (2019, January 16\u201320). Basnet: Boundary-aware salient object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00766"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N. (2018, January 18\u201322). Learning a discriminative feature network for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00199"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cheng, H.K., Chung, J., Tai, Y.-W., and Tang, C.-K. (2020, January 14\u201319). CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual Conference, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00891"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Liu, J.-J., Hou, Q., Cheng, M.-M., Feng, J., and Jiang, J. (2019, January 16\u201320). A simple pooling-based design for real-time salient object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00404"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.1080\/01431161.2019.1699973","article-title":"Learning discriminative spatiotemporal features for precise crop classification from multi-temporal satellite images","volume":"41","author":"Ji","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"2178","DOI":"10.1109\/TGRS.2019.2954461","article-title":"Toward automatic building footprint delineation from aerial images using cnn and regularization","volume":"58","author":"Wei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.compbiomed.2018.10.011","article-title":"Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs","volume":"103","author":"Gu","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1117\/12.603359","article-title":"Validation of the QUick Atmospheric Correction (QUAC) algorithm for VNIR-SWIR multi-and hyperspectral imagery","volume":"5806","author":"Bernstein","year":"2005","journal-title":"Proc. SPIE"},{"key":"ref_33","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). Pytorch: An imperative style, high-performance deep learning library. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 13\u201316). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_35","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 16\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/10\/1912\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:00:28Z","timestamp":1760162428000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/10\/1912"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,13]]},"references-count":36,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13101912"],"URL":"https:\/\/doi.org\/10.3390\/rs13101912","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,13]]}}}