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An adaptive texture selection framework for ultra-high resolution UAV imagery


Kelcey, J and Lucieer, A, An adaptive texture selection framework for ultra-high resolution UAV imagery, Proceedings of 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 21-26 July 2013, Melbourne, Australia, pp. 3883-3886. ISBN 978-1-4799-1114-1 (2013) [Refereed Conference Paper]

Copyright Statement

Copyright 2013 IEEE

DOI: doi:10.1109/IGARSS.2013.6723680


The capacity for additional textural derivatives to compensate for the lack of broader spectral sensitivity of consumer grade digitial cameras is established within a UAV context. A texture selection framework utilising random forest machine learning, was developed for application with ultra-high spatial resolution UAV imagery limited to the visible spectrum. The framework represents an adaptive approach, providing a rapid assessment of different texture measures relative to a specific user-defined application. This framework is illustrated within the context of UAV salt marsh mapping. This study highlights the importance of texture selection for improving classification of UAV imagery exhibiting high local spatial variance.

Item Details

Item Type:Refereed Conference Paper
Keywords:texture, classification, vegetation
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the environmental sciences
UTAS Author:Kelcey, J (Mr Joshua Kelcey)
UTAS Author:Lucieer, A (Professor Arko Lucieer)
ID Code:89838
Year Published:2013
Web of Science® Times Cited:5
Deposited By:Geography and Environmental Studies
Deposited On:2014-03-17
Last Modified:2014-08-05
Downloads:2 View Download Statistics

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