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| Unsupervised Classification Methods and Experimental Research of Dual-frequency Fully Polarimetric SAR Images |
| Liu Xiu-qing①②;Yang Ru-liang①; Yang Zhen①② |
| ①Institute of Electronics Chinese Academy of Sciences Beijing 100080 China;②Graduate School Beijing 100039 China |
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Abstract In this paper, initial assumption of SAR pixel distribution is derived from H/a classifier. Then a Maximum Likelihood (ML) method is introduced to improve the classifi-cation.. The backscattering properties of a natural medium, that varies with the observation frequency, dual-frequency SAR images are combined to get further improved classification. Speckle in SAR images will disturb classification accuracy. Vector filter of speckle is used to dual-frequency images before classification. Experiments are done on data got by NASA/JPL lab near Tien Mountains, and pseudo-colored classification results of both single and dual frequency POLSAR image are submitted. Results show that filtered dual-frequency fully polarimetric SAR data obtain the best classification result.
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Received: 06 June 2003
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2004, Vol. 26 
