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Image segmentation-based oil slick detection using SAR Radarsat-2 OSVN maritime data

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dc.contributor.author Mdakane, Lizwe W
dc.contributor.author Kleynhans, Waldo
dc.contributor.author Schwegmann, Colin P
dc.contributor.author Meyer, Rory GV
dc.date.accessioned 2018-01-15T12:46:10Z
dc.date.available 2018-01-15T12:46:10Z
dc.date.issued 2017-07
dc.identifier.citation Mdakane, L.W. et al. 2017. Image segmentation-based oil slick detection using SAR Radarsat-2 OSVN maritime data. 2017 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), 23-28 July 2017, Fort Worth, USA en_US
dc.identifier.uri DOI10.1109/IGARSS.2017.8127657
dc.identifier.uri http://hdl.handle.net/10204/9966
dc.description Copyright: 2017 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website. en_US
dc.description.abstract Oil spills present a major threat to the sea ecosystem and thus need to be monitored on a regular basis. Synthetic Aperture Radar (SAR) data is well known for ocean monitoring capabilities. SENTINEL 1 (SEN1) extra wide (EW) mode data and RADARSAT-2 (RS2) Maritime Satellite Surveillance Radar (MSSR) modes have been developed to further improve ocean surveillance. This data can monitor large areas (400 km for SEN1 EW and over 500 km for RS2 OSVN), with a finer resolution. These modes enable improved oil slick detection (including ship detection to identify the source) performance while reducing the number of needed scenes. Numerous studies have been proposed for SEN1 data due to its free access while less work has been done on oil spill detection methods using the RS2 OSVN data. In this paper, we evaluated a segmentation-based method on RS2 OSVN data. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Worklist;20063
dc.subject Feature extraction en_US
dc.subject Object classification en_US
dc.subject Bilge waste dumping en_US
dc.subject Synthetic Aperture Radar en_US
dc.title Image segmentation-based oil slick detection using SAR Radarsat-2 OSVN maritime data en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Mdakane, L. W., Kleynhans, W., Schwegmann, C. P., & Meyer, R. G. (2017). Image segmentation-based oil slick detection using SAR Radarsat-2 OSVN maritime data. IEEE. http://hdl.handle.net/10204/9966 en_ZA
dc.identifier.chicagocitation Mdakane, Lizwe W, Waldo Kleynhans, Colin P Schwegmann, and Rory GV Meyer. "Image segmentation-based oil slick detection using SAR Radarsat-2 OSVN maritime data." (2017): http://hdl.handle.net/10204/9966 en_ZA
dc.identifier.vancouvercitation Mdakane LW, Kleynhans W, Schwegmann CP, Meyer RG, Image segmentation-based oil slick detection using SAR Radarsat-2 OSVN maritime data; IEEE; 2017. http://hdl.handle.net/10204/9966 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Mdakane, Lizwe W AU - Kleynhans, Waldo AU - Schwegmann, Colin P AU - Meyer, Rory GV AB - Oil spills present a major threat to the sea ecosystem and thus need to be monitored on a regular basis. Synthetic Aperture Radar (SAR) data is well known for ocean monitoring capabilities. SENTINEL 1 (SEN1) extra wide (EW) mode data and RADARSAT-2 (RS2) Maritime Satellite Surveillance Radar (MSSR) modes have been developed to further improve ocean surveillance. This data can monitor large areas (400 km for SEN1 EW and over 500 km for RS2 OSVN), with a finer resolution. These modes enable improved oil slick detection (including ship detection to identify the source) performance while reducing the number of needed scenes. Numerous studies have been proposed for SEN1 data due to its free access while less work has been done on oil spill detection methods using the RS2 OSVN data. In this paper, we evaluated a segmentation-based method on RS2 OSVN data. DA - 2017-07 DB - ResearchSpace DP - CSIR KW - Feature extraction KW - Object classification KW - Bilge waste dumping KW - Synthetic Aperture Radar LK - https://researchspace.csir.co.za PY - 2017 T1 - Image segmentation-based oil slick detection using SAR Radarsat-2 OSVN maritime data TI - Image segmentation-based oil slick detection using SAR Radarsat-2 OSVN maritime data UR - http://hdl.handle.net/10204/9966 ER - en_ZA


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