In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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1) The same as steps 1~3 in training; 
2) Detect linear targets with fusion operate of D1 and D2 
in testing images; 
3) Utilize AdaBoost classifier in training stagey to get 
Pu\ 
4) Construct MRF model as Eq.l and get optimal 
solution K(0); 
5) Get Pi.TPi with ^-th iterative solution Y (t) ; 
6) Construct MRF model as Eq.4 and get optimal 
solution 
7) Repeat steps 4 and 5 until little changes existing in 
y(*+i) # 
4. EXPERIMENTS 
4.1 Experiments setup 
Experiments are done on SAR image datasets. The datasets and 
parameters are illustrated as following. 
4.1.1 Data: The SAR datasets contains a 1500 X 1200 pixels 
image that are selected from VV polarization SAR images of 
Guangdong Provinces of China in May 2008 of TerraSAR 
satellite. The spatial resolution is 1,25m* 1,25m. Each image of 
the SAR datasets has a ground truth getting from manual 
labeling under ArcGIS software. Our experiments consist of 4 
classed: farmland, woodland, building, water. Half of this 
image is used for training, the remaining for testing. 
4.1.2 Parameters: In linear target detection, the template is 
selected with 15 pixels high, 13 pixels width and 3 pixels centre 
region. The threshold of Dl, D2 and fusion operate are 0.35 
0.45 and 0.35 individually. The minimum region area of 
superpixels in Meanshift based over-segmentation is 400 pixels, 
with spatial bandwidth and range bandwidth are both 3 pixels. 
Features used here are gray histogram and SoftLBP 0. The 
length of sub-lines is 50 pixels and the width of sub-line regions 
is 20 pixels. 
4.2 Classification Performance 
The classification results of the proposed approach in this paper 
are shown in fig.7. Fig.7.c is the beginning of iteration result 
where Pj,TPi = 0, that is without LTP. And there are some 
isolated points in the classification map. Moreover, there are 
many indented edges along the linear targets. In the fig.7.d, e 
and f, isolated points and indented edges decrease gradually 
since the addition of LTP. 
Compared with groundtruth data labeled artificially, 
classification accuracies are listed in table. 1. It shows that the 
average accuracy has been improved only a little from ieration- 
0 to ieration-3, but the overall classification performance has 
large improvement. 
Fig. 7. Experimental Results, (a) original image; (b) groundtruth data with linear targets detected with fusion operate of Dl and D2 operates; (c) 
classification results in iteration 0 (without LTP); (d) classification results in iteration 1 (with LTP); (e) classification results in iteration 1 (with 
LTP); (f) classification results in iteration 1 (with LTP);