Segmentation of Marine Spill Oil SAR Image Based on Gabor, Krawtchouk Moments and KFCM

Abstract:

To further improve the accuracy of SAR image segmentation in the marine spill oil detection, a segmentation method of marine spill oil images based on Gabor, Krawtchouk moments and KFCM is proposed in this paper. Firstly, the marine spill oil image is decomposed by Gabor transform to obtain the texture features of image. Then, the Krawtchouk moments are applied to extract the shape features of image. Finally, the image segmentation is achieved based on KFCM. A large number of experimental results show that, compared with the related segmentation methods such as Tsallis entropy threshold method,CV model method and the method based on Gabor, Krawtchouk moments and FCM, the proposed method can achieve better result.

• Periodical: Advanced Materials Research (Volumes 760-762)
• Pages: 1462-1466
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.760-762.1462
• Online since: September 2013
• Authors: Li Zhu, Yi Quan Wu, Jun Yin
• Keywords: Gabor, Image Segmentation, Kernel Fuzzy C-Means, Krawtchouk Moments, Marine Spill Oil Detection

References

[1] G. W. Wang, Y. Z. Zhang, and H. Lin, A study of oil spill detection using ASAR images, Acta Oceanologica Sinica, vol. 28, no. 4, pp.32-37, (2009).

[2] M. Migliaccio, A. Gambardella, and M. Tranfaglia, SAR polarimetry to observe oil spills, IEEE Transactions Geoscience and Remote Sensing, vol. 45, no. 2, pp.506-511, (2007).

DOI: 10.1109/tgrs.2006.888097

[3] Y. Q. Wu, W. Y. Wu, and Z. Pan, Minimum within-class variance thresholding based on two-dimensional histogram oblique segmentation, Chinese Journal of Scientific Instrument, vol. 29, no. 12, pp.2651-2657, 2008. (in Chinese).

[4] Y. R. Zhou, H. Wang, H. T. Zhu, G. M. Chen, and X.G. Song, Study on segmentation of SAR image for oil spilled at sea, Marine Environmental Science, vol. 28, no. 3, pp.313-315, 2009. (in Chinese).

[5] J. G. Daugman, Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters, Journal of the Optical Society of America, vol. 2, no. 7, p.1160–1169, (1985).

DOI: 10.1364/josaa.2.001160

[6] K. Wu, and H. Z. Shu, Image texture segmentation based on Krawtchouk moment and SVM, Journal of Applied Sciences-Elecctronics and Information Engineering, vol. 26, no. 5, pp.521-525, 2008. (in Chinese).

[7] P. T. Yap, R. Paramesran, and S. H. Omg, Image Analysis by Krawtchouck Moments, IEEE Transactions on Image Processing, vol. 12, no. 11, pp.1367-1377, (2003).

DOI: 10.1109/tip.2003.818019

[8] D. Zhao, G. H. Zhao, and T. H. Wang. Research on spaceflight image segmentation based on fuzzy C-means clustering, Journal of Astronautics, vol. 30, no. 4, pp.1667-1674, 2009. (in Chinese).