lume XXXIX-B3, 2012 
he image. 
  
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 
respectively. With the center pixel as the threshold, its 
neighbors (i.e. intersected pixels) are labeled as 1 (where digital 
number of the neighborhood is larger than that of the center) or 
0 (where digital number of the neighborhood is smaller than 
that of the center). Consequently the number of 1 to 0 or 0 to 1 
gansition &,-1, £,- and & —s for the circle of radius r=1 , r =3 
and r = 5 are computed respectively as follows: 
P, 
£27 X i£-f9-s£. f (1) 
El 
P; 
(o^ X Beg sJ)-sgars (2) 
= 
Ps 
& 7 X sc g) Schr -17 he)| (3) 
kel 
Where, 
P, is the intersected pixels on the perimeter of the circle of 
radius r 71, 
P, is the intersected pixels on the perimeter of the circle of 
radius r 73, 
Ps is the intersected pixels on the perimeter of the circle of 
radius r =5. 
f,,g., h, is the grey values of the center pixel (pc) and fe -g.- he 
and 
1 x»'0 
s(x)= { (4) 
0, x «0 
Finally the total transition &r, is calculated as follows: 
Eo 725.171,35 (5) 
Transition £y, is considered as the LBP value of the center 
pixel. The above arrangement is moved over the whole image 
until all pixels considered. As a result the original image ‘I’ is 
transformed into degree of texture on the basis of its neighbor. 
The transformed image is represented here as D? The 
presented method for selecting the texture feature value using 1 
to 0 or 0 to 1 transitions retains the rotation invariance of the 
texture measurement system, since, the number of transitions 
do not change if the texture is rotated. 
23 Clustering 
The Interactive Self-Organizing Data Analysis Technique 
(ISODATA) method (Jain et al. 1999; Kohei et al. 2007) is 
used to cluster transformed image. It includes three key steps. 
First, assign some arbitrary clustering centers in the image. 
Next, classify each pixel to the nearest cluster. Last, calculate 
all the new cluster centers on the basis of every pixel in one 
cluster set. Step 2 and step 3 are iterative and they stop until the 
change between two iterations is fine or little. During each 
iteration the ISODATA clustering algorithm may have 
refinement by splitting or merging clusters. Clusters are 
merged if either the number of members (pixel) in a cluster is 
less than a certain threshold or if the centers of two clusters are 
closer than a certain threshold. Clusters are split into two 
different clusters if the cluster standard deviation exceeds a 
predefined value and the number of members (pixels) is twice 
the threshold for the minimum number of members. ISODATA 
clustering algorithm has many benefits such as less computing, 
fast computing speed and simplicity as well as un-supervising. 
2.4 Results and Discussion 
“Lucieer et al’s LBP analysis and ISODATA” and “Proposed 
LBP analysis and ISODATA" have been applied on a 3m 
spatial resolution RISAT-II X band microwave image (shown 
in Figure 2a ) of (i) vegetation, (ii) built-up area, and (iii) water 
bodies. Texture is visible in the images. The results of 
“Proposed LBP analysis and ISODATA” method is then 
compared with the results obtained from the analysis based on 
*Lucieer et al's LBP analysis and ISODATA” respectively. 
The “Lucieer et al’s LBP analysis and ISODATA” and 
“Proposed LBP analysis and ISODATA” methods are applied 
on a RISAT-II X-band microwave image are shown in Figure 
2b and 2c. In the output images i.e. in Figure 2b and 2c green, 
blue and brown colors represents agriculture, water bodies and 
built-up area respectively. From the results, it clearly appears 
that the “Lucieer et al’s LBP analysis and ISODATA” method 
gives heterogeneous segments. While “ Proposed LBP 
analysis and ISODATA” method gives more homogeneous 
segments with distinct classes than “Lucieer et al’'s LBP 
analysis and ISODATA" method. 
Using the ground truth data overlaid separately on the resultant 
outputs obtained from “Lucieer et al’s LBP analysis and 
ISODATA” and “Proposed LBP analysis and ISODATA” 
methods, the area statistics of the classification rates for each 
approach is shown in Table 1. The numerical results shows that 
the success rate for recognizing agriculture, built-up area and 
Water bodies are (48.48, 12.23, 58.36) by “Lucieer et al’s 
LBP analysis and ISODATA" whereas (82.55, 73.65 and 
86.20) by the "Proposed LBP analysis and ISODATA" 
approach.