高概率选择和自适应MRF的极化SAR分类

doi:10.3969/j.issn.1001-2400.2017.06.011

Abstract

Abstract:

It is difficult to obtain the accurate boundaries and the smooth regions for polarimetric SAR image classification. In order to solve this problem, a novel classification scheme is proposed that combines the Wishart-based high-probabilistic support vector machine (SVM) and adaptive markov random fields(MRF). First, a Wishart classifier is applied with the probabilistic SVM, according to high-probalistic selection, an initial pixel-based classification result is obtained. Then by combining this result with other edge detection methods, it can access the accurate boundaries. Second, adaptive MRF is proposed based on the edge of the image to further revise the previous classification. In this way, smooth regions are obtained and accurate boundaries are maintained simultaneously. Experimental results show that the proposed method improves the classification performance and that details of the image are preserved.

Key words: SVM, polarimetric synthetic aperture radar, Wishart distance, MRF, adaptive window

ZHANG Shuyin;HOU Biao. POLSAR image classification via high-probability selection and adaptive MRF[J].Journal of Xidian University, 2017, 44(6): 59-64.

References

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POLSAR image classification via high-probability selection and adaptive MRF

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