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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
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Entropy
Values of Regularizing Parameters
Figure 7: Entropy for different classifiers from AWIFS image
From resultant Table 1 and Fig. 7, while monitoring entropy of
fraction images for different regularizing parameter values,
optimum regularizing parameter has been obtained for ‘m’=2.0
and ‘?’=1, which gives highest accuracy (SCM) Le.
96.27%. While using noise clustering without entropy classifier
for fraction image generation fuzzy overall accuracy as well as
fuzzy kappa coefficient is high but uncertainty in these
parameters as well as entropy (absolute indicator of uncertainty)
is also higher. From this work it can be concluded that output
from noise clustering without entropy classifier has higher
classification accuracy with higher uncertainty with respect to
FCM and PCM based classifiers.
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