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30 October 2009 The polarimetric entropy classification of SAR based on the clustering and signal noise ration
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Proceedings Volume 7494, MIPPR 2009: Multispectral Image Acquisition and Processing; 74941H (2009)
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Usually, Wishart H/α/A classification is an effective unsupervised classification method. However, the anisotropy parameter (A) is an unstable factor in the low signal noise ration (SNR) areas; at the same time, many clusters are useless to manually recognize. In order to avoid too many clusters to affect the manual recognition and the convergence of iteration and aiming at the drawback of the Wishart classification, in this paper, an enhancive unsupervised Wishart classification scheme for POLSAR data sets is introduced. The anisotropy parameter A is used to subdivide the target after H/α classification, this parameter has the ability to subdivide the homogeneity area in high SNR condition which can not be classified by using H/α. It is very useful to enhance the adaptability in difficult areas. Yet, the target polarimetric decomposition is affected by SNR before the classification; thus, the local homogeneity area's SNR evaluation is necessary. After using the direction of the edge detection template to examine the direction of POL-SAR images, the results can be processed to estimate SNR. The SNR could turn to a powerful tool to guide H/α/A classification. This scheme is able to correct the mistake judging of using A parameter such as eliminating much insignificant spot on the road and urban aggregation, even having a good performance in the complex forest. To convenience the manual recognition, an agglomerative clustering algorithm basing on the method of deviation-class is used to consolidate some clusters which are similar in 3by3 polarimetric coherency matrix. This classification scheme is applied to full polarimetric L band SAR image of Foulum area, Denmark.
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Lei Shi, Jie Yang, and Fengkai Lang "The polarimetric entropy classification of SAR based on the clustering and signal noise ration", Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74941H (30 October 2009);

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