PDF Arko_Fuzzy_unce...pdf | Request a copy Full text restricted Available under University of Tasmania Standard License.

Abstract

Uncertainty and vagueness are important concepts when dealing with transition
zones between vegetation communities or land-cover classes. In this study, classification
uncertainty is quantified by applying a supervised fuzzy classification
algorithm. New visualization techniques are proposed and presented in order to
come to a better understanding of the relationship between uncertainty in the
spatial extent of image classes and their thematic uncertainty. The thematic extent
of a class is visualized as a three-dimensional (3D) class cluster shape in a featurespace
plot, and the spatial extent of the class is highlighted in an image display
based on a user-defined uncertainty threshold. Changing this threshold updates
both visualizations, showing the effect of uncertainty on the spatial extent of a class
and its shape in feature space. Spheres, ellipsoids, convex hulls, -shapes and
isosurfaces are compared for visualization of 3D class clusters. Isosurfaces are
implemented to facilitate real-time rendering and interaction with class clusters in
feature space. The visualization tool is illustrated with a fuzzy classification of a
Quickbird image of Macquarie Island, one of the unique sub-Antarctic World
Heritage Areas that is characterized by vegetation transition zones. This study
shows that visualization techniques are valuable for the interpretation and
exploration of image classification results and associated uncertainty.

Item Type:	Article
Authors/Creators:	Lucieer, A and Veen, L
Journal or Publication Title:	International Journal of Remote Sensing
ISSN:	0143-1161
DOI / ID Number:	https://doi.org/10.1080/01431160802651942
Additional Information:	The definitive version is available online at http://www.informaworld.com/smpp/content? Copyright © 2009 Taylor & Francis
Item Statistics:	View statistics for this item

Actions (login required)

Item Control Page