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In: Wagner W., Szflcely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010,1 APRS, Vol. XXXVIII, Part 7B 
Fig. 4. (a) Original image; (b) Ratio response map (edges 
probability map); (c) Over-segmentation results 
And then, Meanshift (Dorin Comaniciu, 2002) based over 
segmentation algorithm is employed on the edges probability 
map to divide original images into superpixies (as shown in fig. 
4.c). 
A superpixel captured from pre-segmentation is the smallest 
unit in an image and can be assigned only a class label. Each 
superpixel in images is extracted a set of features consisted of 
gray histogram, SoftLBP (Ahonen T, 2007). 
3.2 Linear Target Prior 
Linear Target Priors (LTP) utilizes the shape of linear target to 
improve the edges of classification results. This prior 
information comes from the relative location between linear 
targets and image pixels (or superpixels) around them. For 
example, we wish to make use of the fact that all pixels 
adjoining river banks are water or farmland (in a certain length). 
Thus, the first is detecting the linear targets in SAR images. In 
this paper, the fusion operates of D1 and D2 operates is 
employed to detect linear targets (edges). And then, the LTP is 
captured in the following ways. 
3.2.1 Distance Map: The distances from points (pixels) to 
lines (linear targets) are calculated as the method presented in 
(Kumar M.P., 2005). Given lines O , the distance 
d = dist(pAl) between point p and Q is the distance 
between point p and point p' which is the nearest point in lines 
Q to point p (as shown in fig.5.a). The distance map is shown 
in fig.5.c. 
I Nil A 
P im ( c l/>;> Q >f (,) ) = ex P Z ¿«list ( A,fT ) Z cont ( c > A- ) 
7=1 
cont ( c, p i , Rf 
1, if Pi and c = maxlabel (Y (,) , ) 
(2) 
other 
Where, p. is LTP weight and maxlabel(l(t), •) is the 
maximum class of pixels in region R = { Rq } of previous 
iteration classification results Y^\ Fig.6 shows an example of 
LTP map for class building, water, farmland and woodland in 
SAR image. These LTP probability values map to the full range 
of values in the cool-hot colormap. 
Fig. 6. (a) linear target prior map for building; (b) linear 
target prior map for water; (c) linear target prior map for 
farmland; (d) linear target prior map for woodland. 
3.3 Iterative MRF Model with LTP 
The posteriori probability of the proposed model is added LTP 
based on Eq.l as shown in Eq.3: 
p(y, \s : )p„(y,\y„)P Lrn (y, k.n.r“) 
P im (y, |s, A7“) = £ P lm (y, | p„0,y<'>) 
Where, s,; is /-th superpixels in image and p,; is a pixel in Sj, 
Pi.TPi is linear targets prior of Sj. The overall image posteriori 
probability is: 
Fig. 5. (a) sketch of distance from pixels to lines; (b) 
linear target map; (c) distance map from pre-pixels to 
linear tagets. 
3.2.2 LTP Map: LTP is learned from the labelled image data 
(classification results of previous iteration in practice), so it 
changes from iteration to next iteration. Firstly, the linear target 
O are divided into sub-lines with a certain length, 
f> = (Oj, ...iTvsi}. And a sub-line il 7 divides its adjacent area 
into K regions, we address them sub-line regions 
R = {R(Qj)} k . Then, the LTP of a pixel p, for class c is 
shown by Eq.2: 
If, 
P{Y\ 1,0., T w ,©) = n/^, k,n,y<->) 
(4) 
T (,+1) = argmaxj/TT 0 ^ | /,Q,T (O ,0)} 
y (t+l) 
A GraphCut-based optimization algorithm presented in Boykov 
Y, 2001 has been used to effectively capture the global optimal 
resolution of Eq.4. The training steps of the proposed Iterative 
MRF model with LTP have been listed in the following: 
1) Utilize edge detection template in fig.3.a to get edges 
probability map of input images; 
2) Over-segment the edges probability map to get 
superpixels; 
3) Extract features in each superpixel; 
4) Training AdaBoost classifier with labeled groundtruth 
data; 
The testing steps of Iterative MRF model with LTP are shown 
in the following: