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The development of deep learning in synthetic aperture radar imagery

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dc.contributor.author Schwegmann, Colin P
dc.contributor.author Kleynhans, Waldo
dc.contributor.author Salmon, BP
dc.date.accessioned 2017-08-22T13:09:37Z
dc.date.available 2017-08-22T13:09:37Z
dc.date.issued 2017-05
dc.identifier.citation Schwegmann, C.P., Kleynhans, W. and Salmon, B.P. 2017. The development of deep learning in synthetic aperture radar imagery. International workshop in Remote Sensing with Intelligent Processing, 19-21 May 2017, Fudan University, 220 Handan Road, Shanghai, China. 10.1109/RSIP.2017.7958802 en_US
dc.identifier.isbn 978-1-5386-1990-2
dc.identifier.uri 10.1109/RSIP.2017.7958802
dc.identifier.uri http://ieeexplore.ieee.org/document/7958802/
dc.identifier.uri http://hdl.handle.net/10204/9465
dc.description Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, kindly consult the publisher's website. en_US
dc.description.abstract The usage of remote sensing to observe environments necessitates interdisciplinary approaches to derive effective, impactful research. One remote sensing technique, Synthetic Aperture Radar, has shown significant benefits over traditional remote sensing techniques but comes at the price of additional complexities. To adequately cope with these, researchers have begun to employ advanced machine learning techniques known as deep learning to Synthetic Aperture Radar data. Deep learning represents the next stage in the evolution of machine intelligence which places the onus of identifying salient features on the network rather than researcher. This paper will outline machine learning techniques as it has been used previously on SAR; what is deep learning and where it fits in compared to traditional machine learning; what benefits can be derived by applying it to Synthetic Aperture Radar imagery; and finally describe some obstacles that still need to be overcome in order to provide constient and long term results from deep learning in SAR. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Worklist;19311
dc.subject Synthetic aperture radar en_US
dc.subject Machine learning en_US
dc.subject Marine technologies en_US
dc.title The development of deep learning in synthetic aperture radar imagery en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Schwegmann, C. P., Kleynhans, W., & Salmon, B. (2017). The development of deep learning in synthetic aperture radar imagery. IEEE. http://hdl.handle.net/10204/9465 en_ZA
dc.identifier.chicagocitation Schwegmann, Colin P, Waldo Kleynhans, and BP Salmon. "The development of deep learning in synthetic aperture radar imagery." (2017): http://hdl.handle.net/10204/9465 en_ZA
dc.identifier.vancouvercitation Schwegmann CP, Kleynhans W, Salmon B, The development of deep learning in synthetic aperture radar imagery; IEEE; 2017. http://hdl.handle.net/10204/9465 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Schwegmann, Colin P AU - Kleynhans, Waldo AU - Salmon, BP AB - The usage of remote sensing to observe environments necessitates interdisciplinary approaches to derive effective, impactful research. One remote sensing technique, Synthetic Aperture Radar, has shown significant benefits over traditional remote sensing techniques but comes at the price of additional complexities. To adequately cope with these, researchers have begun to employ advanced machine learning techniques known as deep learning to Synthetic Aperture Radar data. Deep learning represents the next stage in the evolution of machine intelligence which places the onus of identifying salient features on the network rather than researcher. This paper will outline machine learning techniques as it has been used previously on SAR; what is deep learning and where it fits in compared to traditional machine learning; what benefits can be derived by applying it to Synthetic Aperture Radar imagery; and finally describe some obstacles that still need to be overcome in order to provide constient and long term results from deep learning in SAR. DA - 2017-05 DB - ResearchSpace DP - CSIR KW - Synthetic aperture radar KW - Machine learning KW - Marine technologies LK - https://researchspace.csir.co.za PY - 2017 SM - 978-1-5386-1990-2 T1 - The development of deep learning in synthetic aperture radar imagery TI - The development of deep learning in synthetic aperture radar imagery UR - http://hdl.handle.net/10204/9465 ER - en_ZA


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