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| Unsupervised Classification of Fully Polarimetric SAR Image Using H-α Decomposition and Modified C-Mean Algorithm |
| Wu Yong-hui; Ji Ke-feng; Yu Wen-xian |
| School of Electronics Science and Engineering, National University of Defense Technology, Changsha 410073, China |
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Abstract A new method for unsupervised classification of terrain types using fully POLarimetric Synthetic Aperture Radar (POLSAR) data is proposed in this paper. The method is a combination of the unsupervised classification based on Cloude’s H-α decomposition and the modified C-mean algorithm. The fully polarimetric SAR image is initially classified using Cloude’s method. The classification map is used as input of the modified C-mean algorithm, and then iteration is performed. It is important to determine the number of iteration in the modified C-mean algorithm, and a new termination criterion is presented using image entropy to do so. Compared with H-α decomposition, not only scattering mechanisms of all classes can be preserved, but also terrain classification is effectively performed using this method. The effectiveness of this method is demonstrated using an L-band fully polarimetric SAR image of San Francisco, acquired by the NASA/JPL AIRSAR sensor.
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Received: 06 June 2005
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2007, Vol. 29 
