In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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(g) (h) 
Fig. 2. Flowchart of the proposed model, (a) original image; (b) edges probability map (Ratio response map); (c) over 
segmentation results (superpixels); (d) graph structure for GraphCut segmentation; (e) GraphCut segmentation; (f) segmentation 
results; (g) map of linear target and distance map from pre-pixels to linear targets; (h) linear target prior maps. The iteration 
strategy is marked with dotted lines. 
consistency among homogenous regions, but can hardly 
consider the consistency along the linear targets like roads and 
rivers. 2) Superpixels of Pre-segmentation are captured on 
edges probability maps instead of original images. Since shapes 
of linear targets are always the boundaries of superpixels. In 
this case, more information of edges can be used for 
classification process. 3) Iterative MRF description model is 
more likely to remove noise in classification map compared 
with standard MRF model. 
2. RELATED WORK 
2.1 Edge detection 
Ratio line detector D1 (F.Tupin, 1996) is derived from a 
coupling of two ratio edge detectors on both sides of a region 
(as shown in fig.3.a). Due to multiple responses to a structure, 
detector D1 is not accurate enough to locate the edges. Cross 
correlation line detector D2 (F.Tupin, 1996) utilizes variances 
of regions to improves locating accuracy but with higher 
missing alarm ratio. Tupin (F.Tupin, 1996) merged the 
information from both D1 and D2 in 8 orientations. 
Fig.3. (a) Template and 8 orientations of template used in 
ratio edge detection, (b) Results of ratio edge detection, (c) 
Grouping results, (d) Linear targets detection results 
2.2 AdaBoost based MRF Model 
MRF is a type of classical discriminative model. Given an 
image l = {¿>i, } with Nj pixels or superpixels Si 
and a label set Y = {y\ , jj2--4jNi} with Nc labels, MRF 
model constructs a posteriori probability of Su as shown in Eq.l. 
Where, A > 0 is a constant coefficient. Pyi is prior probability 
and Vij(yi,yj) = 1 when y r — y :i , Vij(yi,Vj) = 0 when 
Ui Y y.j- Phi is always captured by feature-based discriminant 
model like AdaBoost classifier and Pj i% tends to be 
Puiiy-i\f(si)) where f{Si) is the features of Ay. For the whole 
image, the posterior probability is P(Y\I, 0) where 0 is the 
parameter of model. Some optimization algorithms, such as 
GraphCut and Simulated Annealing Algorithm (SAA), can be 
utilized to get maximums of P(Y\I, 0). 
p {y, \s,)« p u {y, I s t ) P Vi (y, | y n ) 
Pn{y,\yvi) = tW\ ¿Z V v(yt>yj) 
V y,en 
P(Y\I,Q) = fjP(y,.\s i ) 
Y = argmax{P(71 /,©)} 
As one of the most popular description model, MRF model can 
balance the likelihood and prior probability in the whole image 
and get global optimal solution with optimization algorithms 
like algorithm presented in (Boykov Y, 2001). So, a linear 
target prior can be introduced into MRF model in this paper 
simply and obviously, see details in section 3. 
3. METHODOLOGY 
Once again, the main motivation of this paper is improving 
region edges in classification results. So, the traditional edge 
detection can get wealth and accurate edge information from 
SAR images which is useful for classification. And, the 
proposed approach in this paper gets use of this information in 
over-segmentation, see details in section 3. 
3.1 Pre-segmentations and Features Extraction 
The proposed approach in this paper begins with pre 
segmentation strategy using over-segmentation method to get 
superpixels. Firstly, we utilize ratio edge detector to get edges 
probability map of each input image (as shown in fig. 4.b).