{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T07:12:39Z","timestamp":1726297959361},"reference-count":29,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T00:00:00Z","timestamp":1677196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"publisher","award":["540034","CRDPJ 543428-19"],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"A common task in land-cover classification is water body extraction, wherein each pixel in an image is labelled as either water or background. Water body detection is integral to the field of urban hydrology, with applications ranging from early flood warning to water resource management. Although traditional index-based methods such as the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) have been used to detect water bodies for decades, deep convolutional neural networks (DCNNs) have recently demonstrated promising results. However, training these networks requires access to large quantities of high-quality and accurately labelled data, which is often lacking in the field of remotely sensed imagery. Another challenge stems from the fact that the category of interest typically occupies only a small portion of an image and is thus grossly underrepresented in the data. We propose a novel approach to data augmentation\u2014pixelwise category transplantation (PCT)\u2014as a potential solution to both of these problems. Experimental results demonstrate PCT\u2019s ability to improve performance on a variety of models and datasets, achieving an average improvement of 0.749 mean intersection over union (mIoU). Moreover, PCT enables us to outperform the previous high score achieved on the same dataset without introducing a new model architecture. We also explore the suitability of several state-of-the-art segmentation models and loss functions on the task of water body extraction. Finally, we address the shortcomings of previous works by assessing each model on RGB, NIR, and multispectral features to ascertain the relative advantages of each approach. In particular, we find a significant benefit to the inclusion of multispectral bands, with such methods outperforming visible-spectrum models by an average of 4.193 mIoU.<\/jats:p>","DOI":"10.3390\/rs15051253","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T06:59:10Z","timestamp":1677481150000},"page":"1253","source":"Crossref","is-referenced-by-count":4,"title":["Water Body Extraction from Sentinel-2 Imagery with Deep Convolutional Networks and Pixelwise Category Transplantation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-4442-0352","authenticated-orcid":false,"given":"Joshua","family":"Billson","sequence":"first","affiliation":[{"name":"Multimedia Research Centre, University of Alberta, Edmonton, AB T6G 2E8, Canada"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2511-3557","authenticated-orcid":false,"given":"MD Samiul","family":"Islam","sequence":"additional","affiliation":[{"name":"Multimedia Research Centre, University of Alberta, Edmonton, AB T6G 2E8, Canada"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3073-4462","authenticated-orcid":false,"given":"Xinyao","family":"Sun","sequence":"additional","affiliation":[{"name":"Multimedia Research Centre, University of Alberta, Edmonton, AB T6G 2E8, Canada"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9699-4895","authenticated-orcid":false,"given":"Irene","family":"Cheng","sequence":"additional","affiliation":[{"name":"Multimedia Research Centre, University of Alberta, Edmonton, AB T6G 2E8, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111338","DOI":"10.1016\/j.rse.2019.111338","article-title":"Remote sensing monitoring of multi-scale watersheds impermeability for urban hydrological evaluation","volume":"232","author":"Shao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, X., and Xie, H. (2018). A Review on Applications of Remote Sensing and Geographic Information Systems (GIS) in Water Resources and Flood Risk Management. Water, 10.","DOI":"10.3390\/w10050608"},{"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":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1109\/LGRS.2018.2886422","article-title":"Automatic and Unsupervised Water Body Extraction Based on Spectral-Spatial Features Using GF-1 Satellite Imagery","volume":"16","author":"Zhang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","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_7","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_8","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":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"7422","DOI":"10.1109\/JSTARS.2021.3098678","article-title":"Deep-Learning-Based Multispectral Satellite Image Segmentation for Water Body Detection","volume":"14","author":"Yuan","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"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 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":"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 (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015). Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Springer International Publishing.","DOI":"10.1007\/978-3-319-24553-9"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Yakopcic, C., Taha, T.M., and Asari, V.K. (2018, January 23\u201326). Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net). Proceedings of the NAECON 2018\u2014IEEE National Aerospace and Electronics Conference, Dayton, OH, USA.","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"ref_16","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2021). Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation. arXiv."},{"key":"ref_17","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention U-Net: Learning Where to Look for the Pancreas. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, 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 (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_19","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, Springer.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_20","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_21","first-page":"102499","article-title":"Multi-scale context extractor network for water-body extraction from high-resolution optical remotely sensed images","volume":"103","author":"Kang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (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_23","doi-asserted-by":"crossref","unstructured":"Schmitt, M., Ahmadi, S.A., and Hansch, R. (2021, January 11\u201316). There is No Data Like More Data - Current Status of Machine Learning Datasets in Remote Sensing. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9555129"},{"key":"ref_24","unstructured":"(2013, December 10). Ulamm. Available online: https:\/\/commons.wikimedia.org\/w\/index.php?curid=30096268\"."},{"key":"ref_25","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., and Cardoso, M.J. Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations. Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support."},{"key":"ref_26","unstructured":"Salehi, S.S.M., Erdogmus, D., and Gholipour, A. Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks. Proceedings of the Machine Learning in Medical Imaging."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Abraham, N., and Khan, N.M. (2019, January 8\u201311). A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation. Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759329"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","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\/15\/5\/1253\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T07:04:24Z","timestamp":1677481464000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,24]]},"references-count":29,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051253"],"URL":"https:\/\/doi.org\/10.3390\/rs15051253","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,24]]}}}
