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Hyperspectral remote sensing has been proven to be an effective tool for monitoring marine oil spills. To make full use of spectral and spatial features, this study proposes a spectral-spatial features integrated network (SSFIN) and applies it for hyperspectral detection of a marine oil spill. Specifically, 1-D and 2-D convolutional neural network (CNN) models have been employed for the extraction of the spectral and spatial features, respectively. During the stage of spatial feature extraction, three consecutive convolution layers are concatenated to achieve the fusion of multilevel spatial features. Next, the extracted spectral and spatial features are concatenated and fed to the fully connected layer so as to obtain the joint spectral-spatial features. In addition, L2 regularization is applied to the convolution layer to prevent overfitting, and dropout operation is employed to the full connection layer to improve the network performance. The effectiveness of the method proposed here has firstly been verified on the Pavia University dataset with competitive classification experimental results. Eventually, the experimental results upon oil spill datasets demonstrate the strong capacity of oil spill detection by this method, which can effectively distinguish thick oil film, thin oil film, and seawater.<\/jats:p>","DOI":"10.3390\/rs13081568","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T06:35:53Z","timestamp":1618814153000},"page":"1568","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A Spectral-Spatial Features Integrated Network for Hyperspectral Detection of Marine Oil Spill"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2565-1013","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Qifan","family":"Shao","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2420-8012","authenticated-orcid":false,"given":"Dongmei","family":"Song","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}]},{"given":"Zhongwei","family":"Li","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Yunhe","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Changlong","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Mingyue","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,18]]},"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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