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IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India,2002 
  
  
  
Fig. 2. (a). IKONOS and (b).IRS-1D imagery selected for the 
present study. (c) and (d) show objects chosen for recognition. 
3.1. Performance evaluation 
The test patches were simulated with specific distortions to 
study the robustness of the proposed approach. Figure 3 gives 
the effect of rotation on the normalized distance between the 
test and query patterns. As can be seen, the curve peaks at 
places where the angles of test pattern are the same as the 
query. 
  
1.00 
0.90 
0.80 4 
0.70 
0 100 200 300 
  
dnorm 
  
  
  
angle of rotation 
  
  
  
Fig.3a. 
Effect of rotation on image identification. 
Similar analyses were carried out to study the effect of scale 
and gaussian noise in the test pattern. Results of these are 
depicted in Figs. 3b and c. As can be seen from Fig. 3b, the 
effect of the scale is asymmetric and the recognition falls quite 
sharply when the magnification factor is less than 0.5. 
  
  
  
| 1.00 
| oso E > 
0.40 : 
norm 
  
  
  
  
magnification 
  
  
Fig:3b. 
Effect of scale variation on object recognition. 
  
1.00 
0.95 
0.90 
? 0.85 
0.80 
norm 
  
0 10 20 30 40 
std. deviation of noise 
  
  
Fig. 3c. Effect on noise on image recognition. 
In all the cases, it can be seen that it is quite necessary to set an 
acceptable threshold for the normalized distance. In our study, 
this threshold was set to a value of 0.75. 
3.2. Application in Content based browsing 
The use of the proposed method for the CBB is shown in Fig. 4. 
In Fig.4a, the test and training objects are taken from the same 
IKONOS image (see Table 1). The left column of Figure 
represents images obtained after the SL-1 stage. There are 
"about 23 windows were hit after the histogram normalization, 
while this number is reduced to just 4 after the SL-2 and 
overlap-window merging. The performance results are 
summarized in Table 4. 
Similar exercise is carried out with the IRS-1D imagery with 
stadium as the object. Here the testing was carried out over the 
image of date of pass other than that of the training data. This 
is mainly to test the robustness of the method for real-world 
variations in the data in both radiometric and geometric sense. 
To demonstrate the tolerance further for detecting objects 
outside the training data, the proposed method was applied to 
an image of another scene. As can be seen from Figs. 3a and 4c, 
the query and test objects are quite different with some 
common features. It can also be seen that while the two 
aircrafts in Fig. 4c are detected, the leftmost aircraft could not 
be identified because of its scale variation falling outside the 
scale limits specified in Table-2.