{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T06:59:22Z","timestamp":1780383562640,"version":"3.54.1"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,3,21]],"date-time":"2020-03-21T00:00:00Z","timestamp":1584748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51709031"],"award-info":[{"award-number":["51709031"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In the case of marine accidents, monitoring marine oil spills can provide an important basis for identifying liabilities and assessing the damage. Shipborne radar can ensure large-scale, real-time monitoring, in all weather, with high-resolution. It therefore has the potential for broad applications in oil spill monitoring. Considering the original gray-scale image from the shipborne radar acquired in the case of the Dalian 7.16 oil spill accident, a complete oil spill detection method is proposed. Firstly, the co-frequency interferences and speckles in the original image are eliminated by preprocessing. Secondly, the wave information is classified using a support vector machine (SVM), and the effective wave monitoring area is generated according to the gray distribution matrix. Finally, oil spills are detected by a local adaptive threshold and displayed on an electronic chart based on geographic information system (GIS). The results show that the SVM can extract the effective wave information from the original shipborne radar image, and the local adaptive threshold method has strong applicability for oil film segmentation. This method can provide a technical basis for real-time cleaning and liability determination in oil spill accidents.<\/jats:p>","DOI":"10.3390\/a13030069","type":"journal-article","created":{"date-parts":[[2020,3,23]],"date-time":"2020-03-23T04:03:01Z","timestamp":1584936181000},"page":"69","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3898-3105","authenticated-orcid":false,"given":"Jin","family":"Xu","sequence":"first","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"},{"name":"Maritime College, Guangdong Ocean University, Zhanjiang 524088, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haixia","family":"Wang","sequence":"additional","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Can","family":"Cui","sequence":"additional","affiliation":[{"name":"Civil Aviation College, Shenyang Aerospace University, Shenyang 110000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baigang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"Maritime College, Guangdong Ocean University, Zhanjiang 524088, China"},{"name":"Laboratory Department, Liaoning Hydrogeology and Engineering Geology Reconnaissance Institute, Dalian 116000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,21]]},"reference":[{"key":"ref_1","unstructured":"Agency, E.S. (2018). Oil Pollution Monitoring. Remote Sensing Exploitation Division, ESRIN\u2014European Space Agency (ESA). Available online: http:\/\/www.esa.int\/esapub\/br\/br128\/br128_1.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zanier, G., Palma, M., Petronio, A., Roman, F., and Armenio, V. (2019). Oil Spill Scenarios in the Kotor Bay: Results from High Resolution Numerical Simulations. J. Mar. Sci. Eng., 7.","DOI":"10.3390\/jmse7020054"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1126\/science.aba2582","article-title":"Brazil oil spill response: Protect rhodolith beds","volume":"367","author":"Sissini","year":"2020","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1126\/science.aba0369","article-title":"Brazil oil spill response: Government inaction","volume":"367","author":"Brum","year":"2020","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hammoud, B., Ndagijimana, F., Faour, G., Ayad, H., and Jomaah, J. (2019). Bayesian Statistics of Wide-Band Radar Reflectionsfor Oil Spill Detection on Rough Ocean Surface. J. Mar. Sci. Eng., 7.","DOI":"10.3390\/jmse7010012"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Atta, A.M., Abdullah, M.M.S., Al-Lohedan, H.A., and Mohamed, N.H. (2019). Novel Superhydrophobic Sand and Polyurethane Sponge Coated with Silica\/Modified Asphaltene Nanoparticles for Rapid Oil Spill Cleanup. Nanomaterials, 2.","DOI":"10.3390\/nano9020187"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2004.11.015","article-title":"Oil spill detection by satellite remote sensing","volume":"95","author":"Brekke","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fingas, M., and Brown, C.E. (2018). A Review of Oil Spill Remote Sensing. Sensors, 18.","DOI":"10.3390\/s18010091"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3649","DOI":"10.1080\/01431161003762371","article-title":"A two-stage registration algorithm for oil spill aerial image by invariants-based similarity and improved ICP","volume":"32","author":"Liu","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1038\/472152a","article-title":"Oil spill: Deep wounds","volume":"472","author":"Schrope","year":"2011","journal-title":"Nature"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5530","DOI":"10.1007\/s11356-016-8214-8","article-title":"Synergistic use of an oil drift model and remote sensing observations for oil spill monitoring","volume":"24","author":"Padova","year":"2017","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4107","DOI":"10.1080\/01431161.2010.484820","article-title":"Robust Satellite Techniques for oil spill detection and monitoring using AVHRR thermal infrared bands","volume":"32","author":"Casciello","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.marpolbul.2014.03.059","article-title":"Review of oil spill remote sensing","volume":"83","author":"Fingas","year":"2014","journal-title":"Mar. Pollut. Bull."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guo, H., Wei, G., and An, J. (2018). Dark Spot Detection in SAR Images of Oil Spill Using Segnet. Appl. Sci., 8.","DOI":"10.3390\/app8122670"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, Y., Yuan, Z., Guo, H., Pan, H., and Guo, J. (2018). Marine Oil Spill Detection Based on the Comprehensive Use of Polarimetric SAR Data. Sustainability, 10.","DOI":"10.3390\/su10124408"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gil, P., and Alacid, B. (2018). Oil Spill Detection in Terma-Side-Looking Airborne Radar Images Using Image Features and Region Segmentation. Sensors, 18.","DOI":"10.3390\/s18010151"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/515247","article-title":"Comparisons of Circular Transmit and Linear Receive Compact Polarimetric SAR Features for Oil Slicks Discrimination","volume":"2015","author":"Yu","year":"2015","journal-title":"J. Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3760","DOI":"10.1109\/JSTARS.2014.2359141","article-title":"The Extended Bragg Scattering Model-Based Method for Ship and Oil-Spill Observation Using Compact Polarimetric SAR","volume":"8","author":"Yin","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"22798","DOI":"10.3390\/s141222798","article-title":"Adaptive Weibull Multiplicative Model and Multilayer Perceptron neural networks for dark-spot detection from SAR imagery","volume":"14","author":"Alireza","year":"2014","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chen, G., Li, Y., Sun, G., and Zhang, Y. (2017). Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images. Appl. Sci., 7.","DOI":"10.3390\/app7100968"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nunziata, F., Buono, A., and Migliaccio, M. (2018). COSMO\u2013SkyMed Synthetic Aperture Radar Data to Observe the Deepwater Horizon Oil Spill. Sustainability, 10.","DOI":"10.20944\/preprints201805.0442.v1"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Guo, H., Wu, D., and An, J. (2017). Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN. Sensors, 17.","DOI":"10.3390\/s17081837"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jones, C.E., and Holt, B. (2018). Experimental L-Band Airborne SAR for Oil Spill Response at Sea and in Coastal Waters. Sensors, 18.","DOI":"10.3390\/s18020641"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6251","DOI":"10.1029\/JC094iC05p06251","article-title":"The Damping of Ocean Waves by Surface Films: A New Look at an Old Problem","volume":"94","author":"Alpers","year":"1989","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1109\/LGRS.2013.2288336","article-title":"Improved Compact Polarimetric SAR Quad-Pol Reconstruction Algorithm for Oil Spill Detection","volume":"11","author":"Yu","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","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_28","first-page":"423","article-title":"Oil spill detection using satellite based SAR - Experience from a field experiment","volume":"59","author":"Bern","year":"1993","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1916","DOI":"10.1109\/36.774704","article-title":"Automatic detection of oil spills in ERS SAR images","volume":"37","author":"Solberg","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","unstructured":"Kanaa, T.F.N., Tonye, E., Mercier, G., Onana, V.P., Ngono, J.M., Frison, P.L., Rudant, J.P., and Garello, R. (2003, January 21\u201325). In Detection of Oil Slick Signatures in SAR Images by Fusion of Hysteresis Thresholding Responses. Proceedings of the 2003 IEEE International Geoscience & Remote Sensing Symposium, Toulouse, France."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.camwa.2014.06.005","article-title":"A global minimization hybrid active contour model with applications to oil spill images","volume":"68","author":"Wang","year":"2014","journal-title":"Comput. Math. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lyu, X. (2018, January 14\u201316). In Oil Spill Detection Based on Features and Extreme Learning Machine Method in SAR Images. Proceedings of the 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE), Huhhot, China.","DOI":"10.1109\/ICMCCE.2018.00123"},{"key":"ref_33","first-page":"421","article-title":"Weibull Multiplicative Model and machine learning models for full-automatic dark-spot detection from SAR images","volume":"40","author":"Taravat","year":"2013","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"95985","DOI":"10.1117\/1.JRS.9.095985","article-title":"Oil spill detection method using X-band marine radar imagery","volume":"9","author":"Zhu","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1007\/s12524-018-0853-4","article-title":"Marine Radar Oil Spill Monitoring Technology Based on Dual-Threshold and C-V Level Set Methods","volume":"46","author":"Xu","year":"2018","journal-title":"J. Indian Soc. Remote"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1080\/15275922.2019.1597781","article-title":"Marine Radar Oil-Spill Monitoring through Local Adaptive Thresholding","volume":"20","author":"Xu","year":"2019","journal-title":"Environ. Forensics"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, P., Li, Y., Liu, B., Chen, P., and Xu, J. (2019). Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding. Remote Sens., 11.","DOI":"10.3390\/rs11070756"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhu, X., Li, N., and Pan, Y. (2019). Optimization Performance Comparison of Three Different Group Intelligence Algorithms on a SVM for Hyperspectral Imagery Classification. Remote Sens., 11.","DOI":"10.3390\/rs11060734"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Phyo, C.N., Zin, T.T., and Tin, P. (2019). Complex Human\u2013Object Interactions Analyzer Using a DCNN and SVM Hybrid Approach. Appl. Sci., 9.","DOI":"10.3390\/app9091869"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, P., and Chen, X. (2019). Intercropping Classification From GF-1 and GF-2 Satellite Imagery Using a Rotation Forest Based on an SVM. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8020086"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"55","DOI":"10.5194\/isprsarchives-XL-1-W3-55-2013","article-title":"Oil spill detection from SAR image using SVM based classification","volume":"1","author":"Matkan","year":"2013","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, Y., Liang, X.S., and Tsou, J. (2017). Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images. Appl. Sci., 7.","DOI":"10.3390\/app7020193"},{"key":"ref_45","unstructured":"Niblack, W. (1986). An Introduction to Digital Image Processing, Prentice Hall."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/S0031-3203(99)00055-2","article-title":"Adaptive document image binarization","volume":"33","author":"Sauvola","year":"2000","journal-title":"Pattern Recognit."},{"key":"ref_47","unstructured":"Bernsen, J. (October, January ). In Dynamic Thresholding of Grey-Level Images. Proceedings of the Eigth International Conference Pattern Recognition, ICPR 1986, Paris, France."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, P., Li, Y., Xu, J., and Zhu, X. (2017). Adaptive enhancement of x-band marine radar imagery to detect oil spill segments. Sensors, 17.","DOI":"10.3390\/s17102349"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/3\/69\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:10:23Z","timestamp":1760173823000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/3\/69"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,21]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["a13030069"],"URL":"https:\/\/doi.org\/10.3390\/a13030069","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,21]]}}}