The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008
Figure 4. Extract edge of a block. Upper: image with initial
curve. Underside: result curve
method is very flexible, we can just extract the edge that we
want and we can have some controls when the curve moves.
Second, when we use edge detectors, sometimes we will meet a
lot of problems of how to track the edges, while using level set
method, the curve is continuous which make it easy to be
tracking. At the end, we did not need to compute how accurate
of the edge would be, but from the image, we can find that the
result is very good.
The disadvantage of this method is also obviously, because the
algorithm of this method is much more complicated that other
methods, it will cost a very long time if running on a low
assembled PC which would limit its application of processing
remote sensing image to a certain degree.
REFERENCES
Dusan Heric,Damjan Zazula, 2007. Combined edge detection
using wavelet transform and signal registration. Image and
Vision Computing, 25(5), pp. 652-662
Ety Navon, Ofer Miller, Amir Arerbuch, 2005. Color image
segmentation based on adaptive local thresholds. Image and
Vision Computing, 23(1), pp. 69-85.
Florence Jacquey, Frédéric Comby, Olivier Strauss, 2008.
Fuzzy edge detection for omnidirectional images. Fuzzy Sets
and Systems, In Press, Uncorrected Proof, Available online 18
March
Jiangping Fan, Guihua Zeng, Mathurin Body,Mohand-Said
Hacid, 2005. Seeded region growing: an extensive and
comparative study. Pattern Recognition, 26(8), pp. 1139-1156.
Li Jiangtao, Ni Guoqiang, Huang Guanghua, 2007. Improved
multiple entropy information segmentation algorithms for
remote sensing images. Optical Technique, 33(4), pp. 543-550.
Lijun Ding, Ardeshir Goshtasby, 2001. On the Canny edge
detector. Pattern Recognition, 34(3), pp. 721-725.
Figure 5. Extract edge of Jiuduansha Shoal in Changjiang
Estuary. Upper: image with initial curve. Underside: result
curve
Liming Hu, H.D. Cheng, MingZhang, 2007. A high
performance edge detector based on fuzzy inference rules.
Information Sciences, 177(21), pp. 4768-4784
M.Emin Yuksel, 2007. Edge detection in noisy images by
neuro-fuzzy processing. AEU-Intemational Journal of
Electronics and Communications, 61(2), pp. 82-89
Ming-Yu,Shih, Din-Chang Tseng, 2005. A wavelet-based
multiresolution edge detection and tracking. Image and Vision
Computing 23(4), pp. 441-451.
Phillip A. Mlsna, Jeffrey J. Rodriguez, 2005. Gradient and
Laplacian Edge Detection. Handbook of Image and Video
Processing (Second Edition).
R.Medina-Camicer, F.J.Madrid-Cuevas, 2008. Unimodal
thresholding for edge detection. Pattern Recognition, 41(7), pp.
2337-2346.
5. CONCLUSIONS
Up to now, there are few papers about processing remote
sensing image using level set method. But there are several
advantages of extract edge using level set method. First, this
Ronald P.Fedkiw, Guillermo Sapiro, Chi-Wang Shu,
2003.Shock capturing,level sets and PDE based methods in
computer vision and image processing: a review of Osher’s
contributions. Journal of Computational Physics, 185(2), pp.
309-341.
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