{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T13:38:31Z","timestamp":1782135511066,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,26]],"date-time":"2021-09-26T00:00:00Z","timestamp":1632614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB0503500"],"award-info":[{"award-number":["2017YFB0503500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Projects of the Central Government Guiding Local Science and Technology Development","award":["2020L3005"],"award-info":[{"award-number":["2020L3005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>It is important for aquaculture monitoring, scientific planning, and management to extract offshore aquaculture areas from medium-resolution remote sensing images. However, in medium-resolution images, the spectral characteristics of offshore aquaculture areas are complex, and the offshore land and seawater seriously interfere with the extraction of offshore aquaculture areas. On the other hand, in medium-resolution images, due to the relatively low image resolution, the boundaries between breeding areas are relatively fuzzy and are more likely to \u2018adhere\u2019 to each other. An improved U-Net model, including, in particular, an atrous spatial pyramid pooling (ASPP) structure and an up-sampling structure, is proposed for offshore aquaculture area extraction in this paper. The improved ASPP structure and up-sampling structure can better mine semantic information and location information, overcome the interference of other information in the image, and reduce \u2018adhesion\u2019. Based on the northeast coast of Fujian Province Sentinel-2 Multispectral Scan Imaging (MSI) image data, the offshore aquaculture area extraction was studied. Based on the improved U-Net model, the F1 score and Mean Intersection over Union (MIoU) of the classification results were 83.75% and 73.75%, respectively. The results show that, compared with several common classification methods, the improved U-Net model has a better performance. This also shows that the improved U-Net model can significantly overcome the interference of irrelevant information, identify aquaculture areas, and significantly reduce edge adhesion of aquaculture areas.<\/jats:p>","DOI":"10.3390\/rs13193854","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T22:16:38Z","timestamp":1632780998000},"page":"3854","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Extraction of Offshore Aquaculture Areas from Medium-Resolution Remote Sensing Images Based on Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Yimin","family":"Lu","sequence":"first","affiliation":[{"name":"Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National Engineering Research Centre of Geospatial Information Technology, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Shao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National Engineering Research Centre of Geospatial Information Technology, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.marpol.2012.10.009","article-title":"Mariculture: A global analysis of production trends since 1950","volume":"39","author":"Campbell","year":"2013","journal-title":"Marine Policy."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1038\/s41559-017-0257-9","article-title":"Mapping the global potential for marine aquaculture","volume":"1","author":"Gentry","year":"2017","journal-title":"Nat. Ecol. Evol."},{"key":"ref_3","unstructured":"Bureau of Fishery and Fishery Administration, Ministry of Rural Agriculture of the People\u2019s Republic of China, National Fisheries Technology Extension Center, and China Society of Fisheries (2020). 2019 China Fischery Statistical Yearbook."},{"key":"ref_4","unstructured":"Luo, G. (2007). Study on the Enviromental Impact Assessment of Aauaculture Plan, Tongji University."},{"key":"ref_5","first-page":"259","article-title":"The Impact of Marine Aquaculture on the Environment; the Importance of Site Selection and Carrying Capacity","volume":"10","author":"Eronat","year":"2019","journal-title":"Agric. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.ocecoaman.2006.06.018","article-title":"Overcoming the impacts of aquaculture on the coastal zone","volume":"49","author":"Primavera","year":"2006","journal-title":"Ocean Coast. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, C., Ji, Y., Chen, J., Deng, Y., Chen, J., and Jie, Y. (2020). Combining Segmentation Network and Nonsubsampled Contourlet Transform for Automatic Marine Raft Aquaculture Area Extraction from Sentinel-1 Images. Remote Sens., 12.","DOI":"10.3390\/rs12244182"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/01431160701250374","article-title":"Auto-extraction technique-based digital classification of saltpans and aquaculture plots using satellite data","volume":"29","author":"Sridhar","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","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_10","first-page":"67522D","article-title":"Extraction of enclosure culture area from SPOT-5 image based on texture feature","volume":"6752","author":"Tang","year":"2007","journal-title":"Geoinformatics"},{"key":"ref_11","first-page":"92","article-title":"Information extraction of floating raft aquaculture based on GF-1","volume":"45","author":"Chu","year":"2020","journal-title":"Surv. Surv. Mapp."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1007\/s00343-019-8265-z","article-title":"Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model","volume":"37","author":"Liu","year":"2019","journal-title":"J. Oceanol. Limnol."},{"key":"ref_13","first-page":"N32B","article-title":"Deep Learning Cyberinfrastructure for Crop Semantic Segmentation","volume":"2019","author":"Sun","year":"2019","journal-title":"AGU Fall Meet. Abstr."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1080\/01431161.2018.1516313","article-title":"Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series","volume":"40","author":"Sun","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","first-page":"102118","article-title":"Satellite-based monitoring and statistics for raft and cage aquaculture in China\u2019s offshore waters","volume":"91","author":"Liu","year":"2020","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2200","DOI":"10.1109\/JSTARS.2020.2990104","article-title":"Deep Learning Classification for Crop Types in North Dakota","volume":"13","author":"Sun","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","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, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"86","DOI":"10.2112\/SI90-011.1","article-title":"Floating Raft Aquaculture Area Automatic Extraction Based on Fully Convolutional Network","volume":"90","author":"Cui","year":"2019","journal-title":"J. Coast. Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fu, Y., Ye, Z., Deng, J., Zheng, X., Huang, Y., Yang, W., Wang, Y., and Wang, K. (2019). Finer resolution mapping of marine aquaculture areas using worldView-2 imagery and a hierarchical cascade convolutional neural network. Remote Sens., 11.","DOI":"10.3390\/rs11141678"},{"key":"ref_20","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial networks. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sui, B., Jiang, T., Zhang, Z., Pan, X., and Liu, C. (2020). A modeling method for automatic extraction of offshore aquaculture zones based on semantic segmentation. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9030145"},{"key":"ref_22","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_23","first-page":"767","article-title":"U-Net Neural Networks and Its Application in High Resolution Satellite Image Classification","volume":"35","author":"Yang","year":"2020","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pan, X., Jiang, T., Zhang, Z., Sui, B., Liu, C., and Zhang, L. (2020). A New Method for Extracting Laver Culture Carriers Based on Inaccurate Supervised Classification with FCN-CRF. J. Mar. Sci. Eng., 8.","DOI":"10.3390\/jmse8040274"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cui, B., Fei, D., Shao, G., Lu, Y., and Chu, J. (2019). Extracting Raft Aquaculture Areas from Remote Sensing Images via an Improved U-Net with a PSE Structure. Remote Sens., 11.","DOI":"10.3390\/rs11172053"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., and Cottrell, G. (2018, January 12\u201315). Understanding convolution for semantic segmentation. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00163"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3575","DOI":"10.1080\/01431161.2019.1706009","article-title":"Research on a novel extraction method using Deep Learning based on GF-2 images for aquaculture areas","volume":"41","author":"Cheng","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","unstructured":"Zhu, C. (2020). Extraction Algorithms of Aquaculture Ponds in Coastal Jones Based on Moderate Resolution Remote Sensing Imagery, Dalian Maritime University."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yu, Z., Di, L., Rahman, M., and Tang, J. (2020). Fishpond Mapping by Spectral and Spatial-Based Filtering on Google Earth Engine: A Case Study in Singra Upazila of Bangladesh. Remote Sens., 12.","DOI":"10.3390\/rs12172692"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, X., You, A., Zhu, Z., Zhao, H., Yang, M., Yang, K., Tan, S., and Tong, Y. (2020, January 23\u201328). Semantic Flow for Fast and Accurate Scene Parsing. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_45"},{"key":"ref_31","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_35","unstructured":"Qilong, W., Banggu, W., Pengfei, Z., Peihua, L., Wangmeng, Z., and Qinghua, H. (2020). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhang, L., Cheng, M., and Feng, J. (2020, January 14\u201319). Strip pooling: Rethinking spatial pooling for scene parsing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00406"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3520","DOI":"10.1109\/TIP.2019.2962685","article-title":"Semantic Segmentation with Context Encoding and Multi-Path Decoding","volume":"29","author":"Ding","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","unstructured":"Chen, L., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_40","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017, January 4\u20139). Automatic differentiation in PyTorch. Proceedings of the 2017 Neural Information Processing Systems, Long Bench, CA, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, L., 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_42","unstructured":"Platt, J. (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, Microsoft Research. Technical Report MSR-TR-98-14."},{"key":"ref_43","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3854\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:05:19Z","timestamp":1760166319000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3854"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,26]]},"references-count":43,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13193854"],"URL":"https:\/\/doi.org\/10.3390\/rs13193854","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,26]]}}}