dc.contributor.author |
Mdakane, Lizwe W
|
|
dc.contributor.author |
Kleynhans, Waldo
|
|
dc.date.accessioned |
2018-01-31T06:50:23Z |
|
dc.date.available |
2018-01-31T06:50:23Z |
|
dc.date.issued |
2017-06 |
|
dc.identifier.citation |
Mdakane, L.W. and Kleynhans, W. 2017. An image-segmentation-based framework to detect oil slicks from moving vessels in the Southern African oceans using SAR imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10(6): 2810-2818 |
en_US |
dc.identifier.issn |
1939-1404 |
|
dc.identifier.uri |
http://ieeexplore.ieee.org/document/7874093/
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|
dc.identifier.uri |
DOI: 10.1109/JSTARS.2017.2671403
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|
dc.identifier.uri |
http://hdl.handle.net/10204/10000
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|
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 slick events caused due to bilge leakage/dumps from ships and from other anthropogenic sources pose a threat to the aquatic ecosystem and need to be monitored on a regular basis. An automatic image-segmentation-based framework to detect oil slick from moving vessels using spaceborne synthetic aperture radar (SAR) images over Southern African oceans was proposed. The study uses an automated threshold-based algorithm and a region-based algorithm to achieve a more efficient oil slick detection. The proposed framework consisted of two parts: First, a threshold-based method was used to detect areas with a high oil slick probability; second, a region-based method was used to extract the full extent of the detected oil slick. The proposed framework was tested on both real SAR and synthetic SAR images and was robust to intensity variations, weak boundaries, and was also more computationally efficient when compared to the region-based method without the threshold-based input. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.ispartofseries |
Worklist;20105 |
|
dc.subject |
Active contoursmodel |
en_US |
dc.subject |
Level set |
en_US |
dc.subject |
Bilge waste dumping |
en_US |
dc.subject |
Oil spills |
en_US |
dc.subject |
Segmentation |
en_US |
dc.subject |
Synthetic aperture radar |
en_US |
dc.subject |
SAR |
en_US |
dc.title |
An image-segmentation-based framework to detect oil slicks from moving vessels in the Southern African oceans using SAR imagery |
en_US |
dc.type |
Article |
en_US |
dc.identifier.apacitation |
Mdakane, L. W., & Kleynhans, W. (2017). An image-segmentation-based framework to detect oil slicks from moving vessels in the Southern African oceans using SAR imagery. http://hdl.handle.net/10204/10000 |
en_ZA |
dc.identifier.chicagocitation |
Mdakane, Lizwe W, and Waldo Kleynhans "An image-segmentation-based framework to detect oil slicks from moving vessels in the Southern African oceans using SAR imagery." (2017) http://hdl.handle.net/10204/10000 |
en_ZA |
dc.identifier.vancouvercitation |
Mdakane LW, Kleynhans W. An image-segmentation-based framework to detect oil slicks from moving vessels in the Southern African oceans using SAR imagery. 2017; http://hdl.handle.net/10204/10000. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Mdakane, Lizwe W
AU - Kleynhans, Waldo
AB - Oil slick events caused due to bilge leakage/dumps from ships and from other anthropogenic sources pose a threat to the aquatic ecosystem and need to be monitored on a regular basis. An automatic image-segmentation-based framework to detect oil slick from moving vessels using spaceborne synthetic aperture radar (SAR) images over Southern African oceans was proposed. The study uses an automated threshold-based algorithm and a region-based algorithm to achieve a more efficient oil slick detection. The proposed framework consisted of two parts: First, a threshold-based method was used to detect areas with a high oil slick probability; second, a region-based method was used to extract the full extent of the detected oil slick. The proposed framework was tested on both real SAR and synthetic SAR images and was robust to intensity variations, weak boundaries, and was also more computationally efficient when compared to the region-based method without the threshold-based input.
DA - 2017-06
DB - ResearchSpace
DP - CSIR
KW - Active contoursmodel
KW - Level set
KW - Bilge waste dumping
KW - Oil spills
KW - Segmentation
KW - Synthetic aperture radar
KW - SAR
LK - https://researchspace.csir.co.za
PY - 2017
SM - 1939-1404
T1 - An image-segmentation-based framework to detect oil slicks from moving vessels in the Southern African oceans using SAR imagery
TI - An image-segmentation-based framework to detect oil slicks from moving vessels in the Southern African oceans using SAR imagery
UR - http://hdl.handle.net/10204/10000
ER -
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en_ZA |