{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T11:27:32Z","timestamp":1780918052842,"version":"3.54.1"},"reference-count":54,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,30]],"date-time":"2022-01-30T00:00:00Z","timestamp":1643500800000},"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":["61890964"],"award-info":[{"award-number":["61890964"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1906217"],"award-info":[{"award-number":["U1906217"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"China University of Petroleum, East China","award":["21CX06057A"],"award-info":[{"award-number":["21CX06057A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Marine oil spills are an emergency of great harm and have become a hot topic in marine environmental monitoring research. Optical remote sensing is an important means to monitor marine oil spills. Clouds, weather, and light control the amount of available data, which often limit feature characterization using a single classifier and therefore difficult to accurate monitoring of marine oil spills. In this paper, we develop a decision fusion algorithm to integrate deep learning methods and shallow learning methods based on multi-scale features for improving oil spill detection accuracy in the case of limited samples. Based on the multi-scale features after wavelet transform, two deep learning methods and two classical shallow learning algorithms are used to extract oil slick information from hyperspectral oil spill images. The decision fusion algorithm based on fuzzy membership degree is introduced to fuse multi-source oil spill information. The research shows that oil spill detection accuracy using the decision fusion algorithm is higher than that of the single detection algorithms. It is worth noting that oil spill detection accuracy is affected by different scale features. The decision fusion algorithm under the first-level scale features can further improve the accuracy of oil spill detection. The overall classification accuracy of the proposed method is 91.93%, which is 2.03%, 2.15%, 1.32%, and 0.43% higher than that of SVM, DBN, 1D-CNN, and MRF-CNN algorithms, respectively.<\/jats:p>","DOI":"10.3390\/rs14030666","type":"journal-article","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T01:46:21Z","timestamp":1643593581000},"page":"666","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill Detection"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4582-1852","authenticated-orcid":false,"given":"Junfang","family":"Yang","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Ma","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yabin","family":"Hu","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongchen","family":"Jiang","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"},{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhua","family":"Wan","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongwei","family":"Li","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.rse.2012.03.024","article-title":"State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill","volume":"124","author":"Leifer","year":"2012","journal-title":"Remote Sens. 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