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Publication
A method to incorporate uncertainty in the classification of remote sensing images
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Advisor(s)
Abstract(s)
The aim of this paper is to investigate if the incorporation of the uncertainty
associated with the classification of surface elements into the classification of
landscape units (LUs) increases the results accuracy. To this end, a hybrid classification
method is developed, including uncertainty information in the classification
of very high spatial resolution multi-spectral satellite images, to obtain a map
of LUs. The developed classification methodology includes the following steps: (1)
a pixel-based hard classification with a probabilistic Bayesian classifier; (2) computation
of the posterior probabilities and quantification of the classification
uncertainty using an uncertainty measure; (3) image segmentation and (4) object
classification based on decision rules. The classification of the resulting objects into
LUs is performed considering a set of decision rules that incorporate the pixelbased
classification uncertainty. The proposed methodology was tested on the
classification of an IKONOS satellite image. The accuracy of the classification was
computed using an error matrix. The comparison between the results obtained
with the proposed approach and those obtained without considering the classification
uncertainty revealed a 12% increase in the overall accuracy. This shows that
the information about uncertainty can be valuable when making decisions and can
actually increase the accuracy of the classification results.
Description
Keywords
Hybrid classification method Uncertainty measures Remote sensing IKONOS satellite image