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 |