{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:00:26Z","timestamp":1776441626716,"version":"3.51.2"},"reference-count":34,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T00:00:00Z","timestamp":1557878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China from MOST; National Natural Science Foundation of China; the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2016YFB0501501; 91638201; XDA19080304"],"award-info":[{"award-number":["2016YFB0501501; 91638201; XDA19080304"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper studies the use of the Fully Convolutional Networks (FCN) model in the extraction of water bodies from Very High spatial Resolution (VHR) optical images in the case of limited training samples. Two different seasonal GaoFen-2 images with a spatial resolution of 0.8 m in the south of the Beijing metropolitan area were used to extensively validate the FCN model. Four key factors including input features, training data, transfer learning, and data augmentation related to the performance of the FCN model were empirically analyzed by using 36 combinations of various parameter settings. Our findings indicate that the FCN-based method can work as a robust and cost-effective tool in the extraction of water bodies from VHR images. The FCN-based method trained on a small amount of labeled L1A data can also significantly outperform the Normalized Difference Water Index (NDWI) based method, the Support Vector Machine (SVM) based method, and the Sparsity Model (SM) based method, even when radiometric normalization and spatial contexts are introduced to preprocess the input data for the latter three methods. The advantages of the FCN-based method are mainly due to its capability to exploit spatial contexts in the image, especially in urban areas with mixed water and shadows. Though the settings of four key factors significantly affect the performance of the FCN based method, choosing a qualified setting for the FCN model is not difficult. Our lessons learned from the successful use of the FCN model for the extraction of water from VHR images can be extended to extract other land covers.<\/jats:p>","DOI":"10.3390\/rs11101162","type":"journal-article","created":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T11:37:40Z","timestamp":1557920260000},"page":"1162","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":107,"title":["Water Body Extraction from Very High Spatial Resolution Remote Sensing Data Based on Fully Convolutional Networks"],"prefix":"10.3390","volume":"11","author":[{"given":"Liwei","family":"Li","sequence":"first","affiliation":[{"name":"The Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Deng Zhuang South Road, Beijing 100094, China"}]},{"given":"Zhi","family":"Yan","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Deng Zhuang South Road, Beijing 100094, China"},{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, No. 2001 Shiji Road, Jiaozuo 454000, China"}]},{"given":"Qian","family":"Shen","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Deng Zhuang South Road, Beijing 100094, China"}]},{"given":"Gang","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, No. 2001 Shiji Road, Jiaozuo 454000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3888-8124","authenticated-orcid":false,"given":"Lianru","family":"Gao","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Deng Zhuang South Road, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0319-7753","authenticated-orcid":false,"given":"Bing","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Deng Zhuang South Road, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, No. 19 (A) Yuquan Road, Shijingshan District, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S1462-0758(99)00009-6","article-title":"Urban hydrology and water management-present and future challenges","volume":"1","author":"Niemczynowicz","year":"1999","journal-title":"Urban Water"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/S0169-555X(01)00107-6","article-title":"China\u2019s 8 challenges to water resources management in the first quarter of the 21st century","volume":"41","author":"Varis","year":"2001","journal-title":"Geomorphology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_4","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_5","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_6","doi-asserted-by":"crossref","unstructured":"Xie, H., Luo, X., Xu, X., Pan, H.Y., and Tong, X.H. (2016). Automated Subpixel Surface Water Mapping from Heterogeneous Urban Environments Using Landsat 8 OLI Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8070584"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hollstein, A., Segl, K., Guanter, L., Brell, M., and Enesco, M. (2016). Ready-to-Use Methods for the Detection of Clouds, Cirrus, Snow, Shadow, Water and Clear Sky Pixels in Sentinel-2 MSI Images. Remote Sens., 8.","DOI":"10.3390\/rs8080666"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5907","DOI":"10.3390\/rs5115907","article-title":"A Water Index for SPOT5 HRG Satellite Imagery, New South Wales, Australia, Determined by Linear Discriminant Analysis","volume":"5","author":"Fisher","year":"2013","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, X.C., Zhao, S.S., Qin, X.B., Zhao, N., and Liang, L.G. (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_10","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2015.10.005","article-title":"Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach","volume":"171","author":"Yang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.02.012","article-title":"Spectral matching based on discrete particle swarm optimization: A new method for terrestrial water body extraction using multi-temporal Landsat 8 images","volume":"209","author":"Jia","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3331","DOI":"10.1080\/01431161.2015.1042594","article-title":"Soft urban water cover extraction using mixed training samples and Support Vector Machines","volume":"36","author":"Sun","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1109\/JSTARS.2015.2420713","article-title":"Combining Pixel- and Object-Based Machine Learning for Identification of Water-Body Types from Urban High-Resolution Remote-Sensing Imagery","volume":"8","author":"Huang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12336","DOI":"10.3390\/rs70912336","article-title":"High-Resolution Mapping of Urban Surface Water Using ZY-3 Multi-Spectral Imagery","volume":"7","author":"Yao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wu, W., Li, Q., Zhang, Y., Du, X., and Wang, H. (2018). Two-Step Urban Water Index (TSUWI): A New Technique for High-Resolution Mapping of Urban Surface Water. Remote Sens., 10.","DOI":"10.3390\/rs10111704"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_17","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2018.2849692","article-title":"GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/TGRS.2018.2864987","article-title":"Scene Classification with Recurrent Attention of VHR Remote Sensing Images","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"4909","DOI":"10.1109\/JSTARS.2017.2735443","article-title":"Surface Water Mapping by Deep Learning","volume":"10","author":"Isikdogan","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2003.10.024","article-title":"Automatic radiometric normalization of multitemporal satellite imagery","volume":"91","author":"Canty","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_27","unstructured":"Teichmann, M., Weber, M., Zoellner, M., Cipolla, R., and Urtasun, R. (2016). Multinet: Real-time joint semantic reasoning for autonomous driving. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1109\/TGRS.2014.2361734","article-title":"A Critical Comparison Among Pansharpening Algorithms","volume":"53","author":"Vivone","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dao, M., Kwan, C., Koperski, K., and Marchisio, G. (2017, January 19\u201321). A joint sparsity approach to tunnel activity monitoring using high resolution satellite images. Proceedings of the 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York, NY, USA.","DOI":"10.1109\/UEMCON.2017.8249061"},{"key":"ref_31","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural network. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, Sardinia, Italy."},{"key":"ref_32","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5280","DOI":"10.1080\/01431161.2014.938181","article-title":"Analysis of the impact of spatial resolution on land\/water classifications using high-resolution aerial imagery","volume":"35","author":"Enwright","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/10\/1162\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:52:09Z","timestamp":1760187129000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/10\/1162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,15]]},"references-count":34,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["rs11101162"],"URL":"https:\/\/doi.org\/10.3390\/rs11101162","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,15]]}}}