{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:02:32Z","timestamp":1769817752117,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"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":["41571336"],"award-info":[{"award-number":["41571336"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Dalian Science and Technology Innovation Foundation","award":["2018J11CY024"],"award-info":[{"award-number":["2018J11CY024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Marine oil spill detection is vital for strengthening the emergency commands of oil spill accidents and repairing the marine environment after a disaster. Polarimetric Synthetic Aperture Radar (Pol-SAR) can obtain abundant information of the targets by measuring their complex scattering matrices, which is conducive to analyze and interpret the scattering mechanism of oil slicks, look-alikes, and seawater and realize the extraction and detection of oil slicks. The polarimetric features of quad-pol SAR have now been extended to oil spill detection. Inspired by this advancement, we proposed a set of improved polarimetric feature combination based on polarimetric scattering entropy H and the improved anisotropy A12\u2013H_A12. The objective of this study was to improve the distinguishability between oil slicks, look-alikes, and background seawater. First, the oil spill detection capability of the H_A12 combination was observed to be superior than that obtained using the traditional H_A combination; therefore, it can be adopted as an alternate oil spill detection strategy to the latter. Second, H(1 \u2212 A12) combination can enhance the scattering randomness of the oil spill target, which outperformed the remaining types of polarimetric feature parameters in different oil spill scenarios, including in respect to the relative thickness information of oil slicks, oil slicks and look-alikes, and different types of oil slicks. The evaluations and comparisons showed that the proposed polarimetric features can indicate the oil slick information and effectively suppress the sea clutter and look-alike information.<\/jats:p>","DOI":"10.3390\/rs13091607","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T21:25:10Z","timestamp":1619040310000},"page":"1607","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Marine Oil Slick Detection Using Improved Polarimetric Feature Parameters Based on Polarimetric Synthetic Aperture Radar Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Guannan","family":"Li","sequence":"first","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"},{"name":"Environmental Information Institute, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"},{"name":"Environmental Information Institute, Dalian Maritime University, Dalian 116026, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3511-1088","authenticated-orcid":false,"given":"Yongchao","family":"Hou","sequence":"additional","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"},{"name":"Environmental Information Institute, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Xiang","family":"Wang","sequence":"additional","affiliation":[{"name":"National Marine Environmental Monitoring Center, Dalian 116026, China"}]},{"given":"Lin","family":"Wang","sequence":"additional","affiliation":[{"name":"National Marine Environmental Monitoring Center, Dalian 116026, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"ref_1","first-page":"91","article-title":"A review of oil spill remote sensing","volume":"2","author":"Fingas","year":"2018","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, G., Li, Y., Liu, B., Hou, Y., and Fan, J. (2018). Analysis of scattering properties of continuous slow-release slicks on the sea surface based on polarimetric synthetic aperture radar. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7070237"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, B., Li, Y., Liu, C., Xie, F., and Muller, J.-P. (2018). Hyperspectral features of oil-polluted sea ice and the response to the contamination area fraction. Sensors, 2.","DOI":"10.3390\/s18010234"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, G., Li, Y., Liu, B., Wu, P., and Chen, C. (2019). Marine oil slick detection based on multi-polarimetric features matching method using polarimetric synthetic aperture radar data. Sensors, 19.","DOI":"10.3390\/s19235176"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3499","DOI":"10.1080\/01431161.2014.905730","article-title":"On the exploitation of polarimetric SAR data to map damping properties of the Deepwater Horizon oil spill","volume":"35","author":"Migliaccio","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5302","DOI":"10.1109\/TGRS.2013.2287916","article-title":"Characterization of marine surface slicks by Radarsat-2 multipolarization features","volume":"9","author":"Skrunes","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1109\/TGRS.2006.888097","article-title":"SAR polarimetry to observe oil spills","volume":"45","author":"Migliaccio","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3812","DOI":"10.1109\/TGRS.2012.2185804","article-title":"Polarimetric analysis of backscatter from the Deepwater Horizon oil spill using L-Band synthetic aperture radar","volume":"10","author":"Minchew","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1109\/LGRS.2014.2363688","article-title":"Characterization of low-backscatter ocean features in dual-copolarization sar using log-cumulants","volume":"12","author":"Skrunes","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","unstructured":"Lee, J.-S., and Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications, CRC Press."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3751","DOI":"10.1109\/JSTARS.2014.2348173","article-title":"Analysis of the polarimetric SAR scattering properties of oil-covered waters","volume":"8","author":"Li","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, B., Perrie, W., Li, W., and Pichel, W.G. (2011). Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image. Geophys. Res. Lett., 38.","DOI":"10.1029\/2011GL047013"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tong, S., Liu, X., Chen, Q., Zhang, Z., and Xie, G. (2019). Multi-feature based ocean oil spill detection for polarimetric sar data using random forest and the self-similarity parameter. Remote Sens., 11.","DOI":"10.3390\/rs11040451"},{"key":"ref_14","unstructured":"Huynen, J.R. (1970). Phenomenological Theory of Radar Targets. [Ph.D. Thesis, Electrical Engineering, Mathematics and Computer Science]."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.marpolbul.2016.07.044","article-title":"Offshore platform sourced pollution monitoring using space-borne fully polarimetric C and X band synthetic aperture radar","volume":"112","author":"Singha","year":"2016","journal-title":"Mar. Pollut. Bull."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1049\/el:20010104","article-title":"Similarity between two scattering matrices","volume":"37","author":"Yang","year":"2001","journal-title":"Electron. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2611","DOI":"10.1016\/j.marpolbul.2011.09.036","article-title":"Oil spill detection with fully polarimetric UAVSAR data","volume":"62","author":"Liu","year":"2011","journal-title":"Mar. Pollut. Bull."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1049\/ip-rsn:20045118","article-title":"Mapping ocean surface features using biogenic slick-fields and SAR polarimetric decomposition techniques","volume":"3","author":"Schuler","year":"2006","journal-title":"IEE Proc. Radar Son. Nav."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhu, X., Li, Y., Zhang, Q., and Liu, B. (2019). Oil film classification using deep learning-based hyperspectral remote sensing technology. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8040181"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3607","DOI":"10.1080\/014311698213849","article-title":"Radar signatures of marine mineral oil spills measured by an airborne multi-frequency radar","volume":"19","author":"Wismann","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","unstructured":"Alpers, W., and Espedal, H. (2004). Oils and surfactants, Synthetic Aperture Radar Marine User\u2019s Manual."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"18851","DOI":"10.1029\/97JC01915","article-title":"Imaging of biogenic and anthropogenic ocean surface films by the multifrequency\/multipolarization SIR-C\/X-SAR","volume":"103","author":"Gade","year":"1998","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5862","DOI":"10.1109\/TGRS.2016.2574561","article-title":"Polarimetric analysis of compact-polarimetry SAR architectures for sea oil slick observation","volume":"54","author":"Buono","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tian, W., Shao, Y., Yuan, J., Wang, S., and Liu, Y. (2010, January 25\u201330). An experiment for oil spill recognition using RADARSAT-2 image. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5652898"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/36.551935","article-title":"An entropy based classification scheme for land applications of polarimetric SAR","volume":"1","author":"Cloude","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1007\/s13131-015-0817-x","article-title":"Research on polarization of oil spill and detection","volume":"35","author":"Cai","year":"2016","journal-title":"Acta Oceanologica Sinica"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1007\/s13131-016-0935-5","article-title":"Oil spill detection by a support vector machine based on polarization decomposition characteristics","volume":"35","author":"Zou","year":"2016","journal-title":"Acta Oceanologica Sinica"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4190","DOI":"10.1109\/TGRS.2017.2690001","article-title":"Analysis of evolving oil spills in full-polarimetric and hybrid-polarity SAR","volume":"55","author":"Espeseth","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3243","DOI":"10.1080\/01431161.2015.1057301","article-title":"SAR polarimetry for sea oil slick observation","volume":"36","author":"Migliaccio","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.05.001","article-title":"Effect of wind direction and incidence angle on polarimetric SAR observations of slicked and unslicked sea surfaces","volume":"213","author":"Skrunes","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wenguang, W., Fei, L., Peng, W., and Jun, W. (2010, January 24\u201328). Oil spill detection from polarimetric SAR image. Proceedings of the 10th IEEE International Conference on Signal Processing Proceedings, Beijing, China.","DOI":"10.1109\/ICOSP.2010.5655943"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_33","first-page":"6859","article-title":"The Jeffries\u2013Matusita distance for the case of complex Wishart distribution as a separability criterion for fully polarimetric SAR data","volume":"35","author":"Dabboor","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1364\/JOSAA.7.002032","article-title":"Contrast in complex images","volume":"7","author":"Peli","year":"1990","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"59801","DOI":"10.1109\/ACCESS.2020.2979219","article-title":"A novel marine oil spillage identification scheme based on convolution neural network feature extraction from fully polarimetric sar imagery","volume":"8","author":"Song","year":"2020","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Nunziata, F., Buono, A., and Migliaccio, M. (2018). COSMO\u2014SkyMed synthetic aperture radar data to observe the deep water horizon oil spill. Sustainability, 10.","DOI":"10.20944\/preprints201805.0442.v1"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Song, D., Ding, Y., Li, X., Zhang, B., and Xu, M. (2017). Ocean oil spill classification with RADARSAT-2 SAR based on an optimized wavelet neural network. Remote Sens., 9.","DOI":"10.3390\/rs9080799"},{"key":"ref_38","first-page":"138","article-title":"Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis","volume":"32","author":"Padma","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","first-page":"18","article-title":"Classification and regression by random forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"8830","DOI":"10.3390\/rs70708830","article-title":"The EnMAP spaceborne imaging spectroscopy mission for earth observation","volume":"7","author":"Guanter","year":"2015","journal-title":"Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"11249","DOI":"10.3390\/rs70911249","article-title":"The EnMAP-Box\u2014A toolbox and application programming interface for EnMAP data processing","volume":"7","author":"Rabe","year":"2015","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1607\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:50:32Z","timestamp":1760161832000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1607"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,21]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13091607"],"URL":"https:\/\/doi.org\/10.3390\/rs13091607","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,21]]}}}