The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi XXXVII. Part B7. Beijing 2008
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distance of the extracted roads position to the reference, and the
optimum value is 0), false alarm rate and missing alarm rate(the
complement sets of the completeness and the correctness
respectively)[5].Used the indexes above, the performance of the
selected various parameters were evaluated(table 1). From table
1 some conclusions in section 2 were verified. Low number of
road region pixel can lead to bad results. The large of the road
length pixel as row 4 in table 1 can lead the increase of the leak
rate. One pixel width and 17 pixel length as middle road region,
at the same time the pixels of number of left region and right
region was set 119 can get desirable result and can not get to
the theoretical 1% false rate alarm due to the change of the
homogeneity. The result in figure 5 is quite satisfied.
[1] LI Su-dan, ZHANG Cui, WANG Zheng-zhi. A study of
road network detection method for SAR images [J]. Journal of
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[2] JIA Cheng-li, KUANG Gang-yao. Automatic Extraction of
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and Graphics, 2005, 10(10):1218-1223.
[3] Ridha Touzi, Armand Lopes, Pierre Bousquet. A Statistical
and Geometrical Edge Detector for SAR Images[J]. IEEE
TRANSACTIONS ON GEOSCIENCE AND REMOTE
SENSING, 1988, 26(6):764~773.
!
Data Preprocessimg
(block the large image,
equivalent looks processing,etc.)
r~~
unsuitable
model parameters chosen
suitable
}
The judgement of homogeneous local region or not by VI
T F } >ST
calculate p p p p calculate(p p A,(p p A
>T
]
labeling roads points
' unsatisfied
the performance evaluation
j satisfied
elimilating holes,connection,etc.
t unsatisfied
Results Assessment
j satisfied
submit
[4] LIU Zhen-hua, YU Wen-zhen, MAO Shi-yi. Compounding
Reconstruction for Synthetic Aperture Radar Images [J]. ACTA
ELECTRONICA SINICA, 2004, 32(3): 363-367.
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Gang-Yao. Automatic Extraction of Road Network from SAR
Imagery Based on Genetic Algorithm [J]. Chinese Journal of
Computers, 2007, 30(7): 1186-1197.
[6] Birgit Wesssel, Christian Wiedemann, Heinrich Ebner. The
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'03. Proceedings[C], 2003 IEEE International, 2003,
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[7] Stefan Hinz. A Fusion Strategy for Extraction of Urban
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[8] Manuele Bicego, Silvio Dalfini, Gianni Vemazza et al.
Automatic Road Extraction from Aerial Images by Probabilistic
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Figure 4: the flowchart of roads extraction from SAR imagery
5. CONCLUSIONS
In the article, we amplified the uncertain factors in the
automatic roads obtained from SAR Imagery. We put forward
the hypothesis of the SAR imagery and then built up the local
road model. From the probability distribution theory, we got the
mathematic function of the model base on the assumption. The
next we analyzed the parameters of the function. From the
uncertain property, we concluded three criteria for the
extraction. Through tests, with the objective performance
evaluation system, use these metrics to continually adjust
uncertain parameters in a real time manner to get a satisfactory
result automatically ,we got one reasonable procedure for the
roads net extraction from the SAR imagery in practice. The
achievements can be applied in image registration, mapping and
GIS applications etc.
[10] XIE Feng, CHEN Ying, LIN Yi .Road extraction from RS
imagery based on wavelet and mathematical
morphology [J]. Computer Engineering and Applications, 2007,
43(22):241-243.
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in Remote Sensing Image Based on Marked Point Process [J].
Geomatics and Information Science of Wuhan University, 2006,
31(1): 59-62.
[14] HUANG Shi-qi, LIU Dai-zhi. Analysis of Some Uncertain
Factors in Spacebome SAR Imaging and SAR Image [J].
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