{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:07:29Z","timestamp":1779494849607,"version":"3.53.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031039478","type":"print"},{"value":"9783031039485","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-03948-5_35","type":"book-chapter","created":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T10:07:17Z","timestamp":1653300437000},"page":"431-443","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Data Transformation for Super-Resolution on Ocean Remote Sensing Images"],"prefix":"10.1007","author":[{"given":"Yuting","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kin-Man","family":"Lam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyu","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muwei","family":"Jian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanjiang","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"issue":"12","key":"35_CR1","doi-asserted-by":"publisher","first-page":"1960","DOI":"10.1109\/LGRS.2016.2618941","volume":"13","author":"Y Yuting","year":"2016","unstructured":"Yuting, Y., Junyu, D., Xin, S., Redouane, L., Muwei, J., Xinhua, W.: Ocean front detection from instant remote sensing SST images. IEEE Geosci. Remote Sens. Lett. 13(12), 1960\u20131964 (2016)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"2","key":"35_CR2","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1109\/LGRS.2017.2780843","volume":"15","author":"Y Yuting","year":"2018","unstructured":"Yuting, Y., Junyu, D., Xin, S., Estanislau, L., Quanquan, M., Xinhua, W.: A CFCC-LSTM model for sea surface temperature prediction. IEEE Geosci. Remote Sens. Lett. 15(2), 207\u2013211 (2018)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"3","key":"35_CR3","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1109\/LGRS.2016.2643000","volume":"14","author":"L Estanislau","year":"2017","unstructured":"Estanislau, L., Xin, S., Junyu, D., Hui, W., Yuting, Y., Lipeng, L.: Learning and transferring convolutional neural network knowledge to ocean front recognition. IEEE Geosci. Remote Sens. Lett. 14(3), 354\u2013358 (2017)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"4","key":"35_CR4","volume":"11","author":"L Estanislau","year":"2017","unstructured":"Estanislau, L., Xin, S., Yuting, Y., Junyu, D.: Application of deep convolutional neural networks for ocean front recognition. J. Appl. Remote Sens. 11(4), 042610 (2017)","journal-title":"J. Appl. Remote Sens."},{"issue":"2","key":"35_CR5","doi-asserted-by":"publisher","first-page":"259","DOI":"10.3390\/rs14020259","volume":"14","author":"Y Yuting","year":"2022","unstructured":"Yuting, Y., Kin-Man, L., Xin, S., Junyu, D., Hanjiang, L.: An efficient algorithm for ocean-front evolution trend recognition. Remote Sens. 14(2), 259 (2022)","journal-title":"Remote Sens."},{"key":"35_CR6","unstructured":"Yuting, Y., Lam, K.M., Junyu, D., Xin, S., Jian, M.: Super-resolution on remote sensing images. In: Proceedings of the International Workshop on Advanced Image Technology, pp. 1\u20135. SPIE (2021)"},{"key":"35_CR7","unstructured":"Yuting, Y., Lam, K.M., Junyu, D., Xin, S., Jian, M.: Application of GoogLeNet for ocean-front tracking. In: Proceedings of the International Workshop on Advanced Image Technology, pp. 1\u20135. SPIE, Hong Kong (2022)"},{"issue":"8","key":"35_CR8","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.1080\/00207160.2012.748895","volume":"90","author":"P Oriol","year":"2013","unstructured":"Oriol, P., Antonio, T., Hussein, Y.: Singularity analysis of digital signals through the evaluation of their unpredictable point manifold. Int. J. Comput. Math. 90(8), 1693\u20131707 (2013)","journal-title":"Int. J. Comput. Math."},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Aur\u00e9lien, D., Ronan, F.: Deep learning for ocean remote sensing: an application of convolutional neural networks for super-resolution on satellite-derived SST data. In: 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS), Cancun, Mexico, pp. 1\u20136. IEEE (2016)","DOI":"10.1109\/PRRS.2016.7867019"},{"issue":"2","key":"35_CR10","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"D Chao","year":"2016","unstructured":"Chao, D., Chen Change, L., Kaiming, H., Xiaoou, T.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"35_CR11","doi-asserted-by":"crossref","unstructured":"Jiwon, K., Jung Kwon, L., Kyoung Mu, L.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, Nevada, USA, pp. 1646\u20131654. IEEE (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"35_CR12","unstructured":"Bee, L., Sanghyun, S., Heewon, K., Seungjun, N., Kyoung Mu, L.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Florida, USA, pp. 136\u2013144. IEEE (2017)"},{"key":"35_CR13","doi-asserted-by":"crossref","unstructured":"Marco, B., Aline, R., Christine, G., Marie, l., Alberi M.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of the British Machine Vision Conference, Surrey, England, pp. 135.1\u2013135.10. BMVA Press (2012)","DOI":"10.5244\/C.26.135"},{"key":"35_CR14","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.pocean.2012.04.003","volume":"105","author":"T Yu-Heng","year":"2012","unstructured":"Yu-Heng, T., Mao-Lin, S., Sen, J., David, E.D., Chia-Ping, C.: Validation of the Kuroshio current system in the dual-domain Pacific Ocean model framework. Prog. Oceanogr. 105, 102\u2013124 (2012)","journal-title":"Prog. Oceanogr."},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"II, L., Chun-Chieh, W., Iam-Fei, P., Dong-Shan, K.: Upper-ocean thermal structure and the western north pacific category 5 typhoons. Part I: ocean features and the category 5 typhoons intensification. Mon. Weather Rev. 136(9), 3288\u20133306 (2008)","DOI":"10.1175\/2008MWR2277.1"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Jia, D., Wei, D., Richard, S., Li-Jia, L., Kai, L., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Florida, USA, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"35_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"key":"35_CR18","unstructured":"Christian, L., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Florida, USA, pp. 4681\u20134690. IEEE (2017)"},{"issue":"2","key":"35_CR19","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/LGRS.2018.2870880","volume":"16","author":"D Junyu","year":"2019","unstructured":"Junyu, D., Ruiying, Y., Xin, S., Qiong, L., Yuting, Y., Xukun, Q.: Inpainting of remote sensing SST images with deep convolutional generative adversarial network. IEEE Geosci. Remote Sens. Lett. 16(2), 173\u2013177 (2019)","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["IFIP Advances in Information and Communication Technology","Intelligent Information Processing XI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-03948-5_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:02:03Z","timestamp":1779494523000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-03948-5_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031039478","9783031039485"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-03948-5_35","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"25 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qingdao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.intsci.ac.cn\/iip2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 reviews per paper","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"56","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"66% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}