ize of an
el image
MxN.
| manual
) for the
> defined
e of the
:uracy of
pep ), 18
(7)
Where / denotes the number of non-zero pixels in Æ . Ideally,
the pep value of a perfect segmentation should equal zero, so
the smaller pep is, the better the segmentation. Thus, pep
indicates the quality of the image segmentation. In this paper,
we use the percentage of correct pixels ( pcp ) to represent the
accuracy of segmentation:
pp =1— pep (8)
Table I compares the efficiency and accuracy of the above
methods for the SAR image.
TABLE I ACCURACY AND EFFICIENCY COMPARISON
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
Method a Ay Az Efficiency- Accuracy —
Time (s) pcp (%)
ALGI 1500 0.05 1.5 49.658 64.821
ALG2 1500 . 0.05 1.5 10.261 81.143
ALG3 2000 0.05 1.5 10.796 79.135
ALG4 1500 0.05 TS 5.184 96.705
Notes: ALG4 is the proposed method.
From the above qualitative analysis (Experiment 1 and 2) and
quantitative analysis (Table I), the following conclusions can be
obtained:
1) By using OTSU algorithm to initialize the level set contour,
segmentation accuracy is improved and iterations can be
reduced significantly (please see the figurel (c)(d) and
ALGI,ALG2 in Table I).
2) Comparing with single-scale segmentation, the multi-scale
technique can produce better accuracy and efficiency (please
see the figurel (d)(f), and ALG2, ALGA in Table I). In any case,
our method (ALG4) often leads to a superior result.
4. CONCLUSION
In this paper, a novel SAR water extraction method integration
multi-scale level sets and OTSU algorithm is proposed.
Although experiments have testified that our method performs
better than previous level set methods, much work remains to be
done. The multi-scale analysis framework is a new component
in the level set method for segmentation and there is still much
work to do in the multi-scale analysis. Further, the Gamma
distribution is used to represent the energy functional, because
of the characteristics of the SAR image; in future work we will
consider a more suitable energy functional for SAR images
produced by different sensors. Finally, a threshold segmentation
method was used here to initialize the level set function, and
was shown to be useful for a binary segmentation; however, for
multiphase segmentation, this technique may require further
development.
5. ACKNOWLEDGEMENTS
This work was supported by National Key Fundamental
Research Plan of China (973) (No.2012CB719906) and
National Natural Fund of China (NSFC) (No.41101414,
No.40901211, and No.61001187).
6. REFERENCES
Ayed, I. B., Vazquez, C., Mitiche, A., and Bellhadj, Z., 2005,
Multi-region level set partitioning of synthetic aperture radar
images, IEEE Transactions on Pattern Analysis and Machine
Intelligence, 27(5), pp. 793-800.
Chan, T. F. and Vese, L. A., 2001, Active contours without edges,
IEEE Transactions on Image Processing, 10(2), pp. 266—277.
Cook, R. Connell I. M. and Oliver, C. J., 1994, MUM
Segmentation for SAR Images, Proceedings of SPIE, 2316, pp.
92-103.
Fjortoft, R., Lopes, A. and Marthon, P., 1998, An Optimal
Multiedge Detector for SAR Image Segmentation, IEEE
Transactions on Geoscience and Remote Sensing, 36(3), pp.
793-802.
Gao, G., Zhao, L. J., Zhang, J., Zhou, D. F. and Huang, J. J., 2008,
A segmentation algorithm for SAR images based on the
anisotropic heat diffusion equation, Pattern Recognition, 41,
pp. 3035-3043.
German, O. and Refregier, P., 2001, Edge location in SAR images:
Performance of the likelihood ratio filter and accuracy
improvement with an active contour approach, IEEE
Transactions on Image Processing, 10(1), pp. 72-78.
Horritt, M. S., 1999, A statistical active contour model for SAR
image segmentation, Image and Vision Computing, 17, pp.
213-224.
Law, Y. N., Lee, H. K. and Yip, A. M., 2008, A multiresolution
stochastic level set method for mumford-shah image
segmentation, /EEE Transactions on Image Processing,
17(12), pp.2289-2300.
Lee, J. S., 1989, Segmentation of SAR Image, IEEE Transaction on
Geoscience and Remote Sensing, 27(6), pp. 981-990.
Li, C., Xu, C., Gui, C. and Fox, M. D., 2005, Level Set Evolution
Without Re-initialization: A New Variational Formulation,
Proceedings of the 2005 IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, 1, pp.1-7.
Martin, P., Refregier, P., Gpidail, F. and Guerault, F., 2004,
Influence of the noise on level set active contour segmentation,
IEEE Transactions on Pattern Analysis and Machine
Intelligence, 26(6), pp. 799-803.
Oliver, C. J., Quegan, S., 1998, Understanding Synthetic Aperture
Radar Images. New York: Artech House.
Osher, S. and Sethian, J., 1988, Fronts Propagating with Curvature
Dependent Speed: Algorithms Based on the Hamilton-Jacobi
Formulation, Journal of Computational Physics, 79(1), pp.
12-49.
Otsu, N., 1979, A threshold selection method from gray-level
histogram, IEEE Trans. on Systems, Man and Cybernetics,
9(1), pp. 62-66.
Silverira, M. and Heleno, S., 2009, Separation Between Water and
Land in SAR Images Using Region-Based Level Sets. IEEE
Geoscience and Remote Sensing Letters, 6(3), pp. 471-475.
Xu, X, Li, D. and Sun, H., 2003, Multiscale SAR Image
Segmentation Using A Double Markov Random Field Model,
Proceedings of Seventh International Symposium on Signal
Processing and Its Applications, 1, pp. 349-352.
Zhao, H.-K., Chan, T., Merriman, B. and Osher, S., 1996, A
variational level set approach to multiphase motion, Journal of
Computer Physics, 127, pp.179—195.
