{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T16:07:23Z","timestamp":1782403643186,"version":"3.54.5"},"reference-count":46,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,6]],"date-time":"2021-03-06T00:00:00Z","timestamp":1614988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["31801619"],"award-info":[{"award-number":["31801619"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFC1402403"],"award-info":[{"award-number":["2016YFC1402403"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Plan of Zhejiang Province, China","award":["2019C02050, and 2020C03012"],"award-info":[{"award-number":["2019C02050, and 2020C03012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Among aquatic biota, corals provide shelter with sufficient nutrition to a wide variety of underwater life. However, a severe decline in the coral resources can be noted in the last decades due to global environmental changes causing marine pollution. Hence, it is of paramount importance to develop and deploy swift coral monitoring system to alleviate the destruction of corals. Performing semantic segmentation on underwater images is one of the most efficient methods for automatic investigation of corals. Firstly, to design a coral investigation system, RGB and spectral images of various types of corals in natural and artificial aquatic sites are collected. Based on single-channel images, a convolutional neural network (CNN) model, named DeeperLabC, is employed for the semantic segmentation of corals, which is a concise and modified deeperlab model with encoder-decoder architecture. Using ResNet34 as a skeleton network, the proposed model extracts coral features in the images and performs semantic segmentation. DeeperLabC achieved state-of-the-art coral segmentation with an overall mean intersection over union (IoU) value of 93.90%, and maximum F1-score of 97.10% which surpassed other existing benchmark neural networks for semantic segmentation. The class activation map (CAM) module also proved the excellent performance of the DeeperLabC model in binary classification among coral and non-coral bodies.<\/jats:p>","DOI":"10.3390\/s21051848","type":"journal-article","created":{"date-parts":[[2021,3,7]],"date-time":"2021-03-07T21:52:15Z","timestamp":1615153935000},"page":"1848","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Development of Coral Investigation System Based on Semantic Segmentation of Single-Channel Images"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1000-4764","authenticated-orcid":false,"given":"Hong","family":"Song","sequence":"first","affiliation":[{"name":"Department of Ocean Engineering and Technology, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan 316021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7727-1859","authenticated-orcid":false,"given":"Syed Raza","family":"Mehdi","sequence":"additional","affiliation":[{"name":"Department of Ocean Engineering and Technology, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan 316021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangfan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Ocean Engineering and Technology, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan 316021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichun","family":"Shentu","sequence":"additional","affiliation":[{"name":"Department of Ocean Engineering and Technology, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan 316021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qixin","family":"Wan","sequence":"additional","affiliation":[{"name":"Department of Ocean Engineering and Technology, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan 316021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Ocean Engineering and Technology, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan 316021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazim","family":"Raza","sequence":"additional","affiliation":[{"name":"Department of Ocean Engineering and Technology, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan 316021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1204-3684","authenticated-orcid":false,"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Ocean Engineering and Technology, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan 316021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s11160-013-9319-5","article-title":"Importance of live coral habitat for reef fishes","volume":"24","author":"Coker","year":"2014","journal-title":"Rev. Fish. Biol. Fish."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1111\/j.1467-2979.2008.00290.x","article-title":"Diversity and functional importance of coral-feeding fishes on tropical coral reefs","volume":"9","author":"Cole","year":"2008","journal-title":"Fish. Fish."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1007\/s10661-009-0855-3","article-title":"Using underwater cameras to assess the effects of snorkeler and SCUBA diver presence on coral reef fish abundance, family richness, and species composition","volume":"163","author":"Dearden","year":"2010","journal-title":"Environ. Monit. Assess."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s10661-006-9239-0","article-title":"Development and application of a video-mosaic survey technology to document the status of coral reef communities","volume":"125","author":"Lirman","year":"2007","journal-title":"Environ. Monit. Assess."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/BF00304070","article-title":"Quantitative video sampling of coral reef benthos: Large-scale application","volume":"14","author":"Carleton","year":"1995","journal-title":"Coral Reefs"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1080\/01431160701408469","article-title":"Mapping of coral reefs using hyperspectral CASI data; a case study: Fordata, Tanimbar, Indonesia","volume":"29","author":"Bertels","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.ecolind.2018.08.052","article-title":"Detection of changes in shallow coral reefs status: Towards a spatial approach using hyperspectral and multispectral data","volume":"96","author":"Bajjouk","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0034-4257(02)00201-8","article-title":"Spectral reflectance of coral reef bottom-types worldwide and implications for coral reef remote sensing","volume":"85","author":"Hochberg","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Beijbom, O., Edmunds, P.J., Roelfsema, C., Smith, J., Kline, D.I., Neal, B.P., Dunlap, M.J., Moriarty, V., Fan, T.Y., and Tan, C.J. (2015). Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0130312"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep learning for visual understanding: A review","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_11","first-page":"7068349","article-title":"Deep learning for computer vision: A brief review","volume":"2018","author":"Athanasios","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sharif, S.M.A., Naqvi, R.A., and Biswas, M. (2020). Learning Medical Image Denoising with Deep Dynamic Residual Attention Network. Mathematics, 8.","DOI":"10.3390\/math8122192"},{"key":"ref_13","first-page":"715","article-title":"Artificial Intelligence-Based Semantic Segmentation of Ocular Regions for Biometrics and Healthcare Applications","volume":"66","author":"Naqvi","year":"2021","journal-title":"CMC-Comput. Mater. Con."},{"key":"ref_14","first-page":"252","article-title":"Classification of Freshwater Zooplankton by Pre-trained Convolutional Neural Network in Underwater Microscopy","volume":"11","author":"Song","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"113688","DOI":"10.1016\/j.envpol.2019.113688","article-title":"Single spectral imagery and faster R-CNN to identify hazardous and noxious substances spills","volume":"258","author":"Hui","year":"2020","journal-title":"Environ. Pollut."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9957","DOI":"10.1364\/AO.56.009957","article-title":"Spectral characteristics of coralline algae: A multi-instrumental approach, with emphasis on underwater hyperspectral imaging","volume":"56","author":"Mogstad","year":"2017","journal-title":"Appl. Opt."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Foglini, F., Angeletti, L., Bracchi, V., Chimienti, G., Grande, V., Hansen, I.M., Meroni, A.N., Marchese, F., Mercorella, A., and Prampolini, M. (2018, January 8\u201310). Underwater Hyperspectral Imaging for seafloor and benthic habitat mapping. Proceedings of the 2018 IEEE International Workshop on Metrology for the Sea, Learning to Measure Sea Health Parameters (MetroSea), Bari, Italy.","DOI":"10.1109\/MetroSea.2018.8657866"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mogstad, A.A., Johnsen, G., and Ludvigsen, M. (2019). Shallow-Water Habitat Mapping using Underwater Hyperspectral Imaging from an Unmanned Surface Vehicle: A Pilot Study. Remote Sens., 11.","DOI":"10.3390\/rs11060685"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Letnes, P.A., Hansen, I.M., Aas, L.M.S., Eide, I., Pettersen, R., Tassara, L., Receveur, J., Floch, S.L., Guyomarch, J., and Camus, L. (2019). Underwater hyperspectral classification of deep-sea corals exposed to 2-methylnaphthalene. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0209960"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5265","DOI":"10.1016\/j.eswa.2010.01.013","article-title":"A novel multi-threshold segmentation approach based on differential evolution optimization","volume":"37","author":"Cuevas","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1109\/TMM.2004.834868","article-title":"Fast Video Segmentation Algorithm with Shadow Cancellation, Global Motion Compensation, and Adaptive Threshold Techniques","volume":"6","author":"Chien","year":"2004","journal-title":"IEEE Trans. Multimed."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xu, Z., Gao, G., Hoffman, E.A., and Saha, P.K. (2012, January 2\u20135). Tensor scale-based anisotropic region growing for segmentation of elongated biological structures. Proceedings of the 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain.","DOI":"10.1109\/ISBI.2012.6235734"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.3390\/rs5041809","article-title":"Image-Based Coral Reef Classification and Thematic Mapping","volume":"5","author":"Shihavuddin","year":"2013","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1109\/4.996","article-title":"Design of an image edge detection filter using the Sobel operator","volume":"23","author":"Kanopoulos","year":"1998","journal-title":"IEEE J. Solid-St. Circ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/0146-664X(75)90012-X","article-title":"A survey of edge detection techniques","volume":"4","author":"Davis","year":"1975","journal-title":"Comput. Gr. Image Process."},{"key":"ref_27","first-page":"15","article-title":"Object enhancement and extraction","volume":"10","author":"Prewitt","year":"1970","journal-title":"Picture Process. Psychopictorics"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A Computational Approach to Edge Detection","volume":"8","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Awalludin, E.A., Hitam, M.S., Yussof, W.N.J.H.W., and Bachok, Z. (2017, January 12\u201314). Modification of canny edge detection for coral reef components estimation distribution from underwater video transect. Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuching, Malaysia.","DOI":"10.1109\/ICSIPA.2017.8120646"},{"key":"ref_30","unstructured":"Pereira, F., Burges, C.J.C., Bottou, L., and Weinberger, K.Q. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25, Curran Associates, Inc."},{"key":"ref_31","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_32","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Maaten, L.V.D., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Springer International Publishing. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-24553-9"},{"key":"ref_36","unstructured":"Yu, F., and Koltun, V. (2016). Multi-Scale Context Aggregation by Dilated Convolutions. arXiv."},{"key":"ref_37","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2016). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. arXiv."},{"key":"ref_38","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":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","article-title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","volume":"Volume 11211","author":"Ferrari","year":"2018","journal-title":"Computer Vision\u2014ECCV 2018"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"King, A., Bhandarkar, S.M., and Hopkinson, B.M. (2018, January 18\u201322). A Comparison of Deep Learning Methods for Semantic Segmentation of Coral Reef Survey Images. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00188"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"0203005","DOI":"10.3788\/IRLA202049.0203005","article-title":"Development of an underwater spectral imaging system based on LCTF","volume":"49","author":"Song","year":"2020","journal-title":"Infrared Laser Eng."},{"key":"ref_43","unstructured":"Yang, T.J., Collins, M.D., Zhu, Y., Hwang, J.J., Liu, T., Zhang, X., Sze, V., Papandreou, G., and Chen, L.C. (2019). DeeperLab: Single-Shot Image Parser. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 27\u201330). Learning Deep Features for Discriminative Localization. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5237","DOI":"10.3390\/su12135237","article-title":"Using Landsat Data to Detect Change in Live to Recently (<6 Months) Dead Coral Cover in the Western Xisha Islands, South China Sea","volume":"12","author":"Zuo","year":"2020","journal-title":"Sustainability"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1080\/14634988.2020.1819715","article-title":"The status of coral reefs in central Vietnam\u2019s coastal water under climate change","volume":"23","author":"Dung","year":"2020","journal-title":"Aquat. Ecosyst. Health Manag."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/5\/1848\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:34:06Z","timestamp":1760160846000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/5\/1848"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,6]]},"references-count":46,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21051848"],"URL":"https:\/\/doi.org\/10.3390\/s21051848","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,6]]}}}