{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T22:10:31Z","timestamp":1772489431755,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,29]],"date-time":"2020-02-29T00:00:00Z","timestamp":1582934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801385"],"award-info":[{"award-number":["41801385"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2018BD004"],"award-info":[{"award-number":["ZR2018BD004"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2019QD010"],"award-info":[{"award-number":["ZR2019QD010"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Technology Research and Development Program of Shandong","doi-asserted-by":"publisher","award":["2019GGX101049"],"award-info":[{"award-number":["2019GGX101049"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Monitoring of offshore aquaculture zones is important to marine ecological environment protection and maritime safety and security. Remote sensing technology has the advantages of large-area simultaneous observation and strong timeliness, which provide normalized monitoring of marine aquaculture zones. Aiming at the problems of weak generalization ability and low recognition rate in weak signal environments of traditional target recognition algorithm, this paper proposes a method for automatic extraction of offshore fish cage and floating raft aquaculture zones based on semantic segmentation. This method uses Generative Adversarial Networks to expand the data to compensate for the lack of training samples, and uses ratio of green band to red band (G\/R) instead of red band to enhance the characteristics of aquaculture spectral information, combined with atrous convolution and atrous space pyramid pooling to enhance the context semantic information, to extract and identify two types of offshore fish cage zones and floating raft aquaculture zones. The experiment is carried out in the eastern coastal waters of Shandong Province, China, and the overall identification accuracy of the two types of aquaculture zones can reach 94.8%. The results show that the method proposed in this paper can realize high-precision extraction both of offshore fish cage and floating raft aquaculture zones.<\/jats:p>","DOI":"10.3390\/ijgi9030145","type":"journal-article","created":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T06:34:23Z","timestamp":1583130863000},"page":"145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["A Modeling Method for Automatic Extraction of Offshore Aquaculture Zones Based on Semantic Segmentation"],"prefix":"10.3390","volume":"9","author":[{"given":"Baikai","family":"Sui","sequence":"first","affiliation":[{"name":"College of Geomatics, Shandong University of Science and Technology, 266590 Qingdao, China"}]},{"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Geomatics, Shandong University of Science and Technology, 266590 Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2300-7112","authenticated-orcid":false,"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geomatics, Shandong University of Science and Technology, 266590 Qingdao, China"}]},{"given":"Xinliang","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Geomatics, Shandong University of Science and Technology, 266590 Qingdao, China"}]},{"given":"Chenxi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Geomatics, Shandong University of Science and Technology, 266590 Qingdao, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1111\/j.1749-7345.2007.00124.x","article-title":"Assessing the economic viability of offshore aquaculture in korea: An evaluation based on rock bream, oplegnathus fasciatus, production","volume":"38","author":"Lipton","year":"2007","journal-title":"J. World Aquacult. Soc."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Al-Nasrawi, A.K.M., Hopley, C.A., Hamylton, S.M., and Jones, B.G. (2017). A Spatio-Temporal Assessment of Landcover and Coastal Changes at Wandandian Delta System, Southeastern Australia. J. Mar. Sci. Eng., 5.","DOI":"10.3390\/jmse5040055"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.aquaculture.2009.03.039","article-title":"Analysis of coastal and offshore aquaculture: Application of the FARM model to multiple systems and shellfish species","volume":"292","author":"Ferreira","year":"2009","journal-title":"Aquaculture"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.spl.2012.08.017","article-title":"Maximum likelihood estimate for the dispersion parameter of the negative binomial distribution","volume":"83","author":"Dai","year":"2013","journal-title":"Stat. Probab. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/TIT.1966.1053873","article-title":"Generalized minimum distance decoding","volume":"12","author":"Forney","year":"1966","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.1109\/83.730379","article-title":"Image segmentation via adaptive k-mean clustering and knowledge-based morphological operations with biomedical applications","volume":"7","author":"Chen","year":"1998","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/36.124218","article-title":"Classification of multispectral remote sensing data using a back-propagation neural network","volume":"30","author":"Heermann","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1007\/BF03168418","article-title":"Automatic lung nodule detection using profile matching and back-propagation neural network techniques","volume":"6","author":"Freedman","year":"1993","journal-title":"J. Digit. Imaging"},{"key":"ref_9","first-page":"1","article-title":"Support vector machine","volume":"1","author":"Saunders","year":"2002","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_10","first-page":"562","article-title":"Method of remote sensing image fine classification based on geometric features and svm","volume":"500","author":"Zhou","year":"2012","journal-title":"Constr. Build. Mater."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1016\/S0031-3203(99)00137-5","article-title":"Genetic algorithm-based clustering technique","volume":"33","author":"Maulik","year":"2000","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1016\/j.mcm.2009.10.023","article-title":"Remote sensing image classification by the chaos genetic algorithm in monitoring land use changes","volume":"51","author":"Guo","year":"2010","journal-title":"Math. Comput. Model"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1109\/TNNLS.2013.2293637","article-title":"On the Complexity of Neural Network Classifiers: A Comparison between Shallow and Deep Architectures","volume":"25","author":"Bianchini","year":"2014","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional neural networks for large-scale remote-sensing image classification","volume":"55","author":"Maggiori","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/LGRS.2018.2799877","article-title":"Polsar image classification using polarimetric-feature-driven deep convolutional neural network","volume":"15","author":"Chen","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","first-page":"59","article-title":"Offshore aquatic farming areas extraction method based on aster data","volume":"2011","author":"Ma","year":"2010","journal-title":"Trends Parasitol."},{"key":"ref_18","first-page":"1","article-title":"Automatic mapping aquaculture in coastal zone from TM imagery with OBIA approach","volume":"2","author":"Zhang","year":"2010","journal-title":"Int. Geol. Rev."},{"key":"ref_19","first-page":"486","article-title":"A method of coastal aquaculture area automatic extraction with high spatial resolution images","volume":"30","author":"Lu","year":"2015","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fan, J., Chu, J., Jie, G., and Zhang, F. (2015). Floating raft aquaculture information automatic extraction based on high resolution SAR images. IEEE Int. Geosci. Remote Sens. Symp.","DOI":"10.1109\/IGARSS.2015.7326676"},{"key":"ref_21","first-page":"593","article-title":"Research on marine floating raft aquaculture sar image target recognition based on deep collaborative sparse coding network","volume":"42","author":"Geng","year":"2016","journal-title":"Acta Autom. Sin."},{"key":"ref_22","first-page":"640","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Long","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_24","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_25","first-page":"357","article-title":"Semantic image segmentation with deep convolutional nets and fully connected crfs","volume":"4","author":"Chen","year":"2014","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_26","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":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"016502","DOI":"10.1117\/1.JRS.14.016502","article-title":"Semantic segmentation of very high-resolution remote sensing image based on multiple band combinations and patchwise scene analysis","volume":"14","author":"Zhang","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_29","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Bing, X., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_31","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 Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). 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":"Li, H., Lin, Z., Shen, X., Brandt, J., and Hua, G. (2015, January 7\u201312). A convolutional neural network cascade for face detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299170"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Papandreou, G., Kokkinos, I., and PierreAndr\u00e9, S. (2015, January 7\u201312). Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298636"},{"key":"ref_35","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on International Conference on Machine Learning, Lille, France."},{"key":"ref_36","first-page":"347","article-title":"A maximum-likelihood approach to visual event classification","volume":"96","author":"Siskind","year":"1996","journal-title":"IET Comput. Vis."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/S1532-0464(03)00034-0","article-title":"Logistic regression and artificial neural network classification models: A methodology review","volume":"35","author":"Dreiseitla","year":"2002","journal-title":"J. Biomed. Inform."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/3\/145\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:02:49Z","timestamp":1760173369000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/3\/145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,29]]},"references-count":37,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["ijgi9030145"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9030145","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,29]]}}}