Mdakane, Lizwe WKleynhans, WaldoSchwegmann, Colin PMeyer, Rory GV2018-01-152018-01-152017-07Mdakane, 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, USADOI10.1109/IGARSS.2017.8127657http://hdl.handle.net/10204/9966Copyright: 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.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.enFeature extractionObject classificationBilge waste dumpingSynthetic Aperture RadarConference PresentationMdakane, 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/10204/9966Mdakane, 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/10204/9966Mdakane 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 .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 -