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An adaptive texture selection framework for ultra-high resolution UAV imagery
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.
History
Publication title
Proceedings of 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)Editors
C Fraser, J Walker and M WilliamsPagination
3883-3886ISBN
978-1-4799-1114-1Department/School
School of Geography, Planning and Spatial SciencesPublisher
IEEEPlace of publication
USAEvent title
2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)Event Venue
Melbourne, AustraliaDate of Event (Start Date)
2013-07-21Date of Event (End Date)
2013-07-26Rights statement
Copyright 2013 IEEERepository Status
- Restricted