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

conference contribution
posted on 2023-05-23, 08:30 authored by Kelcey, J, Arko LucieerArko Lucieer
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 Williams

Pagination

3883-3886

ISBN

978-1-4799-1114-1

Department/School

School of Geography, Planning and Spatial Sciences

Publisher

IEEE

Place of publication

USA

Event title

2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Event Venue

Melbourne, Australia

Date of Event (Start Date)

2013-07-21

Date of Event (End Date)

2013-07-26

Rights statement

Copyright 2013 IEEE

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in the environmental sciences

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    University Of Tasmania

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