The second search-level, SL-2 of Fig.1, includes the feature 
extraction of objects based on Gabor filters. These features are 
compared with those of the query pattern for taking final 
decision. 
2.1. Processing at Search Level 1 (SL-1) 
As mentioned above, aim of this search is to reduce the number 
of regions in which a detailed search is to be followed. This is 
accomplished by means of histogram matching of query and 
test objects. It is important to note that the query and test 
objects are acquired in different times of imaging, thus leading 
to variations in contrast and brightness between them. The 
histograms are therefore to be first normalized, and is taken 
care by the following equations: If. (44,04) and (W,,0,) are their 
means and standard deviations of query and test objects 
respectively, then the new gray value for test patch will be 
G""tm, n) 2 G""(m, n) x slope + offset. (1) 
Where G represents the gray value at (m, n) pixel location, the 
factor slope is given by (04/0) and the offset given by (uu, — 
slope X u, ). This amounts to bring the histogram of the test 
object close to that of the query object. It is now left to compare 
these histograms to realize quick search of possible regions 
where the objects of interest lie. The method adapted here for 
comparing them is a normalized histogram intersection 
approach given by [7]. 
hag (tq) = > min (heil, ha iD /A [1]. (2) 
Where h,[i] and h,[i] denote test and query histograms. The 
probable patches thus obtained in this step are passed to SL-2 
processing (Fig.1) and, is described below. 
2.2. Processing at Search-level-2 (SL-2) 
The first step in the SL-2 module is to estimate the Gabor filter 
coefficients of the test object patch, and compare them with 
those of the query object. This is achieved by decomposing the 
given image patch /(x,y) into the a set of Gabor wavelets. For 
the sake of completeness, basic Gabor function and wavelets 
are described briefly here. For full details, the reader is referred 
to Manjunath and Ma [4]. 
The Gabor wavelet transform of a given image /(x,y) is defined 
as 
Wmmn(u,v) = J 1(x,y) Gmn*(u-x,v-y)dxdy. (3) 
Where * denotes complex conjugate, and the intervals of 
integration extend from - æ to + æ, The functions G are a set of 
non-orthogonal wavelets obtained from its mother wavelet : 
íi 2 2 
di 1 PHASE pem 
210,0, 2) 10 re 
(4) 
Where 4 represents frequency of sinusoidal plane wave, and 
(0, 0,) the space constants respectively in x and y directions. 
From these a class of self-similar wavelets are generated that 
would represent features of the object by dilating (scaling) and 
rotating G(x,y) of Eqn. (4) as Gym(X,y) = a”” G(x’,y’). Here, the 
scale factor a is greater than 1, (m,n) are integers and the 
transformed co-ordinates (x’,y’) are given by — [a"(x cosO + 
ysinO) and a^ (-x sinO — y cos0)]. The angle 6 is given by 
  
IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring", Hyderabad, India,2002 
  
(nz/K), with K representing the total number of orientations. 
Once the wavelets are computed, their mean and standard 
deviations can be used as measures of feature components for 
the object. These measures are given by 
mn = J Won(x,)] 2 dx dy 
and On” 2 J/|W,ux;y)-Mas| ^ dxdy. (5) 
Thus, the feature vector of an object is represented by the 
components of Eqn. (5). In this work, we have used 24 pairs of 
these components (corresponding to four scales and six 
orientations of Gabor wavelets), much similar to Manjunath 
and Ma. 
3. RESULTS AND DISCUSSIONS 
To validate the above method, experiments were carried out on 
the IKONOS PAN image of 1m. resolution (size: 512 x512) 
and IRS-1D PAN image of 5.8 m. resolution (300 x 300 ) 
presented in Fig. 2. From these images, an aircraft and a 
stadium were selected as query objects. Details of the 
experimentation are given in Table 1. Firstly, the performance 
of the proposed method for different distortions was evaluated 
through simulation. Secondly, tolerance of multi-date data for 
same and/or alike objects was also evaluated. 
Table 1. Details of experimentation. 
  
  
  
  
  
  
  
Data Object experimentation 
IKONOS Aircraft Training/evaluation 
IRS-1D Stadium Training done one data 
/evaluation with another 
data set — same object 
IKONOS Aircraft Training with one data- 
evaluation with another 
data set- alike object 
  
  
As shown in Fig. 1, when each search window is treated 
independently for the object search, and since search window is 
shifted by half of its width each, it is, highly possible that the 
objects are hit in more than one window. These overlapped 
windows are merged in to a single patch. This is achieved by 
first estimating the centroid of each such window and by 
minimizing the energy difference between this and the query 
object. Image entropy has been used as a measure of energy for 
this purpose. 
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