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Unsupervised classification of fully polarimetric SAR data based on non-Gauss distribution |
| XU Bin |
| Department of Electronic Engineering, Sichuan Information Technology College, Guangyuan 608040, China |
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Abstract In this paper, an unsupervised algorithm is proposed to classify the data of fully polarimetric synthetic aperture Radar(PolSAR). The proposed method combines the Freeman-Durden with the scattering model based development of the decomposition algorithm and the K-Wishart distribution based on non Gauss. This is mainly composed of three steps. The first is the application of Freeman-Durden decomposition of the pixel to divide the scattering into three types: surface scattering, volume scattering and dihedral scattering, and then by using the shape parameter the scattering type can be divided into three types. After that, the eight neighborhood priori probabilities for each pixel are calculated to improve the classification distance and calculate the cluster centers. Finally, the iterative K-Wishart classifier is applied to PolSAR image for accurate classification and the color padding scheme. Different from Wishart distribution, the K-wishart distribution is not only suitable for uniform regional data description, but also very strong for the general uneven regional data description. The experiment results show that the proposed method has better classification performance than Freeman-Durden decomposition and complex Wishart distribution.
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| Keywords
Loess Plateau
remote sensing
vegetation coverage
returning farmland to forest and windbreak
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Issue Date: 03 May 2017
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2017,
Vol. 29
