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)