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Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches

Hamylton, SM, Morris, RH, Carvalho, RC, Roder, N, Barlow, P, Mills, K and Wang, L 2020, Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches, International Journal of Applied Earth Observation and Geoinformation, vol. 89, pp. 1-14, doi: 10.1016/j.jag.2020.102085.

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Title Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches
Author(s) Hamylton, SM
Morris, RH
Carvalho, RCORCID iD for Carvalho, RC orcid.org/0000-0003-0790-5614
Roder, N
Barlow, P
Mills, K
Wang, L
Journal name International Journal of Applied Earth Observation and Geoinformation
Volume number 89
Article ID 102085
Start page 1
End page 14
Total pages 14
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2020
ISSN 1569-8432
0303-2434
Keyword(s) Science & Technology
Technology
Remote Sensing
Lomandra
Convolutional neural network
Five Islands nature reserve
Language eng
DOI 10.1016/j.jag.2020.102085
Indigenous content off
Field of Research 0909 Geomatic Engineering
0406 Physical Geography and Environmental Geoscience
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30137018

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.