{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T22:50:02Z","timestamp":1781304602521,"version":"3.54.1"},"reference-count":35,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2018,9,1]],"date-time":"2018-09-01T00:00:00Z","timestamp":1535760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671481"],"award-info":[{"award-number":["61671481"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Qingdao Applied Fundamental Research","award":["16-5-1-11-jch"],"award-info":[{"award-number":["16-5-1-11-jch"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["18CX05014A"],"award-info":[{"award-number":["18CX05014A"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2018,9]]},"DOI":"10.1109\/tgrs.2018.2803038","type":"journal-article","created":{"date-parts":[[2018,2,23]],"date-time":"2018-02-23T19:28:53Z","timestamp":1519414133000},"page":"4973-4988","source":"Crossref","is-referenced-by-count":50,"title":["Oil Spill Segmentation via Adversarial $f$ -Divergence Learning"],"prefix":"10.1109","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8941-2698","authenticated-orcid":false,"given":"Xingrui","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9860-2901","authenticated-orcid":false,"given":"Chunbo","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hairong","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3949-985X","authenticated-orcid":false,"given":"Peng","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2015.2490078"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2011.2162960"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JOE.2016.2520216"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/JOE.2016.2565018"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126474"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/OCEANSE.2017.8084923"},{"key":"ref10","first-page":"2672","article-title":"Generative adversarial networks","author":"goodfellow","year":"2014","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref12","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume":"37","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref13","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TIP.2008.2002304","article-title":"Minimization of region-scalable fitting energy for image segmentation","volume":"17","author":"li","year":"2008","journal-title":"IEEE Trans Image Process"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/JOE.2017.2722225"},{"key":"ref16","first-page":"1","article-title":"Rectifier nonlinearities improve neural network acoustic models","author":"maas","year":"2013","journal-title":"Proc ICML Workshop Deep Learn Audio Speech Lang Process"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.274"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2017.2671403"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2006.888097"},{"key":"ref28","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2574561"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2015.2497716"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2005.847753"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2015.2401041"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2006.887019"},{"key":"ref5","first-page":"288","author":"chandler","year":"1987","journal-title":"Introduction to Modern Statistical Mechanics"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2690001"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/36.868885"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2013.2267594"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1080\/014311600750037589"},{"key":"ref1","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume":"70","author":"arjovsky","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2012.2185804"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1214\/08-AOS595"},{"key":"ref21","first-page":"2672","article-title":"Conditional generative adversarial nets","author":"mirza","year":"2014","journal-title":"Proc Neural Inf Process Syst Deep Learn Represent Learn Workshop"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2007.4423048"},{"key":"ref23","first-page":"271","article-title":"f-GAN: Training generative neural samplers using variational divergence minimization","author":"nowozin","year":"2016","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref26","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2016","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/IKT.2014.7030329"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/8447182\/08301576.pdf?arnumber=8301576","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T15:02:15Z","timestamp":1643209335000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8301576\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9]]},"references-count":35,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tgrs.2018.2803038","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9]]}}}