04 
as 
)% 
on 
lon 
Des 
118 
cal 
„of 
the 
led 
for 
ble 
| in 
nat 
0S 
Veg 
o 1.6% 
fo 28.3% 
fo 0.1% 
ho 0.0% 
% 0.4% 
% 12.2% 
b 304% 
% 
% 27.2% 
A am 
% 426% 
5 3890 
nat 
bs veg 
NEE eem 
X 35% 
% 0.6% 
% 0.1% 
% 0.3% 
5% 1.3% 
% 82.8% 
py M5 
3% 965 
35 3890 
best 
rubs 
and 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
« Better distinction between cultivated areas and ‘other’; 
4.3 Convergent belief values 
Distributions of CBV of the different classes are presented in 
Figure 2. This distribution reflects a hierarchy among these 
classes: 
1. Crops present dominancy of high CBVs in which summer 
crops (cotton and sunflower) exhibit very high proportions of 
high belief level (PHBL; ~95%), whereas winter crops (wheat 
and legumes) gained only moderate PHBLs (68% and 51% 
respectively). 
2. Orchards presented a mixture of high, medium and low CBV 
figures. 
3. Natural vegetation areas and shrubs/forest areas present 
dominancy of low and poor levels, with low proportions of 
PHBL. 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
100% à ru pump pU EAS m 
14% 10% 
of of 
2 80% 51% 44% 22% 
X 68% 34% 9 
a 94% er 
€ 0, 0 
5 60% - 95% 11% 
= 
o 
E 40% - 
o 
= 57% 
[3] 
© 20% 4 
œ 
0% Ei ; E en T “a ; ATUM ; S ; i i 
Qo Q^ > © x xe S 
SS S e S « A 
3 à «S SS © S 
CS SF X 28 oS e I 
Epoor  (0%-50%) Dlow  (50%-65%) 
D medium (6596-8096) D high (80956-10096) 
Figure 2: Proportions of cumulative belief levels. 
In spite of this, as presented in Table 3 orchards, shrubs and 
natural vegetation achieved by the KBS accuracy of around 
80% and reliability of 95%. These accuracy and reliability 
figures leads to a proposition that medium and low CBVs do 
not necessarily imply for wrong classification decisions by the 
KBS. 
S. DISCUSSION 
Relationships between recognition accuracy/reliability of both 
classification methods and CBVs for each class were assessed. 
Two characteristics of a class have attracted attention through 
the analysis of these relationships: 
Heterogeneity: In similar way to ecological characterization of 
species diversity, the CBVs attributed to pixels of a certain 
land-cover class, represent the different variants of this class. 
Heterogeneity of an ecological system is examined among other 
indexes by its species diversity. Analogues to that, 
heterogeneity of a class may be examined through its CBV 
diversity. CBV diversity of a certain class was measured 
according to Shannon-Weiner information index: 
20 
CBV Diversity (CBVD) - *. p; *In p; (1) 
i=} 
where / stands for the 5% intervals of the CBV (e.g., i=1 is 0%- 
5% CBV and i=20 is 95%-100%) and p stands for the 
proportion of each interval relative to the overall class. 
919 
  
CBVD PHBL US-CEM KBS-CEM 
Cotton 0.75 95% 94% 95% 
Sunflower 1.17 94% 91% 92% 
Wheat 2.00 68% 78% 88% 
Legume 2.53 51% 67% 88% 
Orchards 2.19 37% 65% 77% 
Shrubs 2.42 14% 29% 81% 
Nat_Veg 2.76 1096 30% 83% 
  
Table 4: Values of CBVD, PHBL, US-CEM and KBS-CEM 
for each class. 
  
  
deba boar ly = 0.085% + 0.0005 
= 8 R? = 0.5862 
TT} 8 
Q » = 
> = = 
o 
5 475 © m 
= 
E y = -0.2974x + 1.2361 ° o 
c R? = 0.6915 
9 
= 05 
o 
= i 
o o US-CEM | 
a = KBS-CEM doe 
0.25 
0.5 1 1.5 2 2.5 3 
Heterogeniety Index - CBVD 
Figure 3: Relationships between heterogeneity index 
(CBVD) and classification efficiency measures (CEM) of US 
and KBS classifications. 
A high CBVD is expected when there is a wide range of CBV 
values attributed to a class, which indicates heterogeneity and, 
conversely, a low CBVD is expected for cases in which there is 
dominancy of a certain signature. As inferred from Table 4, 
summer crops are very homogeneous, whereas wheat, orchards, 
legumes and shrubs are more heterogeneous. It may be 
hypothesized that as class heterogeneity increases, the 
recognition ability of a classification decreases. This hypothesis 
is partially supported by the classification results. When the 
classification efficiency (CEM) is regarded as a measure 
representing the lower value between accuracy and reliability of 
each class, there was found moderate correlations (1? = 0.69) 
between CBVD and CEM for the US and lower for the KBS (1 
— 0.59; Figure 3). However, the US classification efficiency is 
highly more affected by the heterogeneity. Slope of linear 
trend-line of the US is five times higher then this of the KBS 
(0.3 vs 0.065). These moderate correlations indicate that 
heterogeneity alone does not fully characterize the limitedness 
of the US classification, and there is a need to analyze how 
unique is each class. : 
Uniqueness: is represented by the PHBL obtained for each 
class (Table 4). Wherever a class is composed solely of unique 
variants it gains a relatively high PHBL (e.g., cotton) as there 
are negligible conflicts in most of its pixels. High correlation (r^ 
— 0.94) was found between the CEM of the US and the PHBL 
(Figure 4). In addition to the moderate correlation found with 
the CBVD it can be concluded that the success of an ‘off-the- 
shelf US classification diminishes with increasing 
heterogeneity of a class, and to a greater extent than its 
diminution with decreasing uniqueness. 
In contrast, the CEM of the KBS presented moderate 
correlation with PHBL (Figures 4), and with five times lower