XXIX-B3, 2012 
1e correspondence 
SS-III pixels (here 
pixel of AWIFS) 
essment. The flow 
in Fig. 1. The six 
orest, eucalyptus 
crop, agriculture 
t crop have been 
collected with the 
while taking 100 
mly selected. 
ried to find out the 
or FCM and PCM 
entation on noise 
ere it has tried to 
arameter (?) with 
kappa coefficient. 
n taken from 1 to 
eighting exponent 
racy, fuzzy kappa 
meters have been 
data sets. It has 
r increases, fuzzy 
coefficient also 
also observed that 
i given Fig. 4 and 
be the appropriate 
. noise clustering 
  
    
  
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
5. RESULTS AND DISCUSSIONS 
The uncertainty is a significant issue in the classification of 
remote sensing data. The uncertainty estimation of the 
classification results is important and necessary to evaluate 
the classifier performance. In this paper, we addressed the 
evaluation of FCM, PCM and Noise Clustering without 
Entropy 
based classifier, while estimating uncertainty in fuzzy overall 
accuracy and fuzzy kappa coefficient with varying spatial 
resolution of classification and reference sub-pixel outputs. 
The uncertainty criteria have been estimated from SCM 
matrix based on actual and desired outputs of classifier. 
Therefore, these criteria are dependent on the error of the 
results and sensitive to error variations. So it has also been 
tried to estimate entropy, based on actual outputs of classifier 
and hence is sensitive to uncertain variations. 
In this research work performance of each classifier was 
estimated based on overall accuracy, fuzzy kappa coefficient, 
uncertainty in overall accuracy and fuzzy kappa coefficient 
and entropy mentioned in Fig 3, 5, 7 respectively. In this 
paper, we addressed the evaluation of FCM classifier, PCM 
classifier and noise clustering without entropy while 
estimating uncertainty and overall accuracy from SCM and 
fuzzy kappa coefficients shown in Table 1 and Fig. 3, 4, 5 
and 6 for AWIFS with LISS-III. From the Fig. 7 it is clear 
that the entropy is higher for AWIFS in case of PCM 
classifier. 
6. CONCLUSION 
In this research work performance of each classifier was 
estimated based on overall accuracy, fuzzy kappa coefficient, 
uncertainty in overall accuracy and fuzzy kappa coefficient 
and entropy. It has been tried to generate fraction outputs 
from FCM, PCM, and noise clustering without entropy. 
These outputs have been generated from AWIFS as well as 
LISS-III images of IRS-P6 data. Fuzzy overall accuracy and 
fuzzy kappa coefficient are relative accuracy assessment but 
entropy is an absolute uncertainty indicator. 
  
100 
90 | & 
80 
70 - 
60 
50 - 
Overall Accuracy (9o 
40 - fi Be Gee Be e 
30 + 
Table 1: Overall maximum fuzzy accuracy from 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 
different classifiers with optimum parameters 
  
  
  
  
  
  
  
  
  
  
5 > » 
9 ~~ N 7 N = > 
; E |<E = E| 38, | £2 | € 
o S 5 0 cs £5 es = 
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= en BT py M2 22 m 
3 B =5 | E= RS = 2 N 
= N NS ST NS Sc m 
© S S58i558| 29 5E z 
= & 3 3 2 |. 9S 
Eb 8 £0 B < 
o 
rx 
FCM 31 7758 | 1193 | 0.700 0.165 | 0.01 
PCM 1.4 45.41 | 30.65 | 0.0919 | 0.623 0.14 
Noise 20 
Clusteri aod 
ng 96.27 0.29 0.494 | 0.058 0.71 
; delta 
without zl 
Entropy 
  
  
The following legends are used in graphs. 
seis 
FOAM 
oo. 535TH A 
Pe 
NU- ulus Egtson 
ee BW a Bids op 
wo BH -W ot Entropy 
ss HON mbent Entgopy 
m dede RÀ 
Values of Regulanzing Parameter 
Figure 3: Overall Accuracy for different c 
v iwucel 
cO NC-Wubent Entopy Bor Ce Ht 
pw CMI 
y Dor 93M: 
tar 2-13 
  
E 
14 15 1617 18 19.2 21 22 23 24 25 26 2.7 283 29 3 31 32 
lassifiers of AWIFS with LISS-III