Abstract
In this paper, the IHSL transform and the Fuzzy C-Means (FCM) segmentation algorithm are combined together to perform the unsupervised classification for fully polarimetric Synthetic Aperture Rader (SAR) data. We apply the IHSL colour transform to H/α/SPAN space to obtain a new space (RGB colour space) which has a uniform distinguishability among inner parameters and contains the whole polarimetric information in H/α/SPAN. Then the FCM algorithm is applied to this RGB space to finish the classification procedure. The main advantages of this method are that the parameters in the color space have similar interclass distinguishability, thus it can achieve a high performance in the pixel based segmentation algorithm, and since we can treat the parameters in the same way, the segmentation procedure can be simplified. The experiments show that it can provide an improved classification result compared with the method which uses the H/α/SPAN space directly during the segmentation procedure.
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References
E. Pottier, S. R. Cloude. A review of target decomposition theorems in radar polarimetry. IEEE Trans. on Geosci. and Remote Sensing, 34(1996)2, 498–518.
E. Pottier. Unsupervised classification scheme and topography derivation of PolSAR data based on the H/α/A polarimetric decomposition theorem. Proceedings of the 4th International Workshop on Radar Polarimetry, Nates, France, July 1998, 535–548.
J. S. Lee, M. R. Grunes, et al. Unsupervised classification using polarimetric decomposition and the complex Wishart classifier. IEEE Trans. on Geosci. and Remote Sensing, 37(1999)5, 2249–2258.
E. Pottier. IGARSS’05 tutorial for polarimetric SAR data analysis, July 2005.
K. Kimura, Y. Yamaguchi, H. Yamada. Unsupervised land cover classification using H/α/TP space applied to POLSAR image analysis. IEICE Transactions on Communications, E87-B(2004)6, 1639–1647.
K. Kimura, Y. Yamaguchi, H. Yamada Pi-SAR image analysis using polarimetric scattering parameters and total power. in Proc. IGARSS’03, Toulouse, France, July 2003, vol.1, 425–427.
F. Cao, W. Hong. A new classification method based on Cloude-Pottier eigenvalue/eigenvector decompositio. in Proc. IGARSS’05, Korea, July 2005.
Commission Internationale de l’Eclairage. International Lighting Vocabulary. Number 17.4. CIE, 4th edition, 1987.
K. R. Castleman, Digital Image Processing. Prentice Hall, Englewood Cliffs, NJ, USA, 1996, Sec. 21.3.
K. N. Plataniotis, A. N. Venetsanopoulos. Color image processing and applications. Springer-Verlag Berlin Heidelberg 2000, Sec. 1.8 and 6.2.
A. Hanbury, J. Serra. A 3D-polar coordinate colour representation suitable for image analysis. http://www.prip.tuwien.ac.at, July 2003.
J. C. Bezdek. Fuzzy mathematics in pattern classification. [Ph.D. dissertation], Cornel University, Ithaca, N. Y.
J. C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, 1981.
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Wu, Y., Cao, F. & Hong, W. An unsupervised classification for fully polarimetric SAR data using SPAN/H/αISHL transform and the FCM algorithm. J. of Electron.(China) 24, 145–149 (2007). https://doi.org/10.1007/s11767-005-0231-6
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DOI: https://doi.org/10.1007/s11767-005-0231-6
Key words
- IHSL transform
- Fuzzy C-Means (FCM) segmentation
- Fully polarimetric Synthetic Aperture Rader (SAR) data
- Unsupervised classification