XXXIX-B3, 2012
-111
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
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.
7. REFERENCES
Binaghi, E. and Rampini, A., 1993. Fuzzy decision making in
the classification of multisource remote sensing data. Optical
Engineering 6, 1193-1203.
Bezdek, J.C., 1980. A convergence theorem for the fuzzy
ISODATA clustering algorithms. IEEE Trans. on Pattern Anal.
Machine Intell., PAMI-2(1): 1-8.
Bezdek, J.C., Ehrlich, R., and Full, W., 1984. FCM: the fuzzy
c-means clustering algorithm.Computers & Geosciences, 10(2-
3): 191-203.
Bezdek, J.C., Hathaway, R.J., Sabin, M.J., and Tucker, W.T.,
1987. Convergence theory for fuzzy c-means: counterexamples
and repairs. IEEE Trans. on Syst, Man, Cybern, SMC-
17(5):873-877.
Bezdek, J. C. 1981. Pattern Recognition with Fuzzy Objective
Function Algorithms, Plenum, New York, USA.
Dave, R. N. 1990. Fuzzy-shell clustering and applications to
circle detection in digital images, Int. J. General Syst., vol. 16,
pp. 343-355.
Dave, R.N., 1991. Characterization and detection of noise in
clustering, Pattern Recognition Letters 12, 657-664.
Dave R. N. and Krishnapuram, R., 1997. Robust Clustering
Methods: A Unified View. IEEE Transactions on Fuzzy
Systems, VOL. 5, NO. 2, May.
Dave, R.N., Sen., 1997. " On Generalizing the Noise Clustering
Algorithms"; 7^ IFSA World Congress; Prague ; pp 205-210.
Fisher P, 1997. The pixel: a snare and a delusion, International
Journal of Remote Sensing, vol 18, no.3, pp 679-685.
Foody G. M., 1995, Cross-entropy for the evaluation of the
accuracy of a fuzzy land cover classification with fuzzy ground
data. ISPRS Journal of Photogrammetry and remote sensing,
50, 2-12.
Foody, G. M., 1996, Approaches for the production and
evaluation of fuzzy land cover classifications from remotely
sensed data. International Journal of Remote Sensing, vol. 17,
no. 7, pp. 1317-1340.
Foody, G M 2000. Estimation of sub-pixel land cover
composition in the presence of untrained classes, Computers
and Geosciences, vol 26,pp 469-478.
Frank, R. and, Frank K. Rudolf Kruse, 2007 ;” A novel
approach to noise clustering for outlier detection”; Soft
Compute, Springer-Verlag; pp 489-494Krishnapuram, R and
Keller, J M 1993. A possibilistic approach to clustering, IEEE
Transactions on Fuzzy System, vol 1,no. 2,pp 98-110.
Kumar, A., Ghosh, S. K. and Dadhwal, V. K., 2006. Study of
Sub-Pixel Classification Algorithms for High Dimensionality
Data Set, IEEE International Geoscience And Remote Sensing
Symposium and 27th Canadian Symposium on Remote
Sensing, Denver, Colorado, USA, 31 July — 04 August.
Silván-Cárdenas, J. L., and Wang L., 2007. Sub-pixel
confusion-uncertainty matrix for assessing soft classifications,
Remote Sensing of | Environment (2007), doi:
10.1016/j.rse.2007.07.017.
