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Small ships don't shine: classification of ocean vessels from low resolution, large swath area SAR acquisitions
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| dc.contributor.author |
Meyer, Rory GV
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| dc.contributor.author |
Kleynhans, Waldo
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| dc.contributor.author |
Schwegmann, Colin P
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| dc.date.accessioned | 2017-05-17T08:46:34Z | |
| dc.date.available | 2017-05-17T08:46:34Z | |
| dc.date.issued | 2016-07 | |
| dc.identifier.citation | Meyer, R.G.V., Kleynhans, W. and Schwegmann, C.P. 2016. Small ships don't shine: classification of ocean vessels from low resolution, large swath area SAR acquisitions. IGARSS 2016: Advancing the Understanding of Our Living Planet, 10-15 July 2016, Beijing, China. DOI: 10.1109/IGARSS.2016.7729247 | en_US |
| dc.identifier.isbn | 978-1-5090-3332-4 | |
| dc.identifier.issn | 2153-7003 | |
| dc.identifier.uri | DOI: 10.1109/IGARSS.2016.7729247 | |
| dc.identifier.uri | http://ieeexplore.ieee.org/document/7729247/ | |
| dc.identifier.uri | http://hdl.handle.net/10204/9099 | |
| dc.description | Copyright: 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 | Monitoring ocean vessels that are not near the coast is difficult and expensive. One way of overcoming this is through the use of SAR satellite platforms. To monitor the largest possible area would require the use of course resolution SAR images which reduce even the largest ships to several pixels. This paper covers the datasets, methods and results arrive at a machine learning algorithm that classifies the size of a v1essel from course resolution SAR images. EW-GRDM Sentinel-1A images were used to classify ocean vessels by size. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartofseries | Worklist;18074 | |
| dc.subject | Satellite Synthetic Aperture Radar | en_US |
| dc.subject | SAR | en_US |
| dc.subject | Sentinel-1A | en_US |
| dc.subject | Maritime domain awareness | en_US |
| dc.title | Small ships don't shine: classification of ocean vessels from low resolution, large swath area SAR acquisitions | en_US |
| dc.type | Conference Presentation | en_US |
| dc.identifier.apacitation | Meyer, R. G., Kleynhans, W., & Schwegmann, C. P. (2016). Small ships don't shine: classification of ocean vessels from low resolution, large swath area SAR acquisitions. IEEE. http://hdl.handle.net/10204/9099 | en_ZA |
| dc.identifier.chicagocitation | Meyer, Rory GV, Waldo Kleynhans, and Colin P Schwegmann. "Small ships don't shine: classification of ocean vessels from low resolution, large swath area SAR acquisitions." (2016): http://hdl.handle.net/10204/9099 | en_ZA |
| dc.identifier.vancouvercitation | Meyer RG, Kleynhans W, Schwegmann CP, Small ships don't shine: classification of ocean vessels from low resolution, large swath area SAR acquisitions; IEEE; 2016. http://hdl.handle.net/10204/9099 . | en_ZA |
| dc.identifier.ris | TY - Conference Presentation AU - Meyer, Rory GV AU - Kleynhans, Waldo AU - Schwegmann, Colin P AB - Monitoring ocean vessels that are not near the coast is difficult and expensive. One way of overcoming this is through the use of SAR satellite platforms. To monitor the largest possible area would require the use of course resolution SAR images which reduce even the largest ships to several pixels. This paper covers the datasets, methods and results arrive at a machine learning algorithm that classifies the size of a v1essel from course resolution SAR images. EW-GRDM Sentinel-1A images were used to classify ocean vessels by size. DA - 2016-07 DB - ResearchSpace DP - CSIR KW - Satellite Synthetic Aperture Radar KW - SAR KW - Sentinel-1A KW - Maritime domain awareness LK - https://researchspace.csir.co.za PY - 2016 SM - 978-1-5090-3332-4 SM - 2153-7003 T1 - Small ships don't shine: classification of ocean vessels from low resolution, large swath area SAR acquisitions TI - Small ships don't shine: classification of ocean vessels from low resolution, large swath area SAR acquisitions UR - http://hdl.handle.net/10204/9099 ER - | en_ZA |
