加权全极化SAR图像非监督Wishart分类方法
doi: 10.3724/SP.J.1146.2007.00950
Weighted-based Unsupervised Wishart Classification of Fully Polarimetric SAR Image
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摘要: 为提高基于极化目标分解与复Wishart非监督分类方法中对不同类别地物中心散射相关矩阵的估值精度与合理性，本文提出了加权全极化SAR图像非监督Wishart分类方法，该方法通过对求解每一类地物散射相关矩阵时，进行数值加权，使得求解的散射相关矩阵更能代表地物类别的中心。本文详细阐述了该方法的原理和实施步骤，并通过对AIRSAR的L波段实际数据进行分类实验，可知该加权算法无论在分类精确度上还是在迭代速度上，性能都有所提高。
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关键词:
- 极化SAR /
- 加权 /
- 非监督Wishart分类
Abstract: For improving the estimated precision and rationality of the scattering coherency matrixes of the class center of different terrain types among the unsupervised classification based on polarimetric target decomposition and the maximum likelihood classifier based on the complex Wishart distribution, a new method for unsupervised classification of terrain types is proposed in this paper. Through weighted-based calculating coherency matrix of the center of every class, the coherency matrix can better represent the class center because the method well considers the correlation of pixels and the texture information of SAR image. The algorithm is described in detail and the contrastive experiment is done using AIRSAR L band polarimetric images. The experiment result indicates that the classifications accuracy of the method is higher and the iterative speed is faster. -
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