An unsupervised hidden Markov random field based segmentation of polarimetric SAR images

Biplab Banerjee, S. De, S. Manickam, A. Bhattacharya
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引用次数: 1

Abstract

This paper proposes an iterative unsupervised Markov Random Field (MRF) based segmentation technique for polarimetric Synthetic Aperture Radar (SAR) image using the optimized scattering mechanism similarity parameters. Parameter estimation for the MRF model is generally performed from the available training data in order to perform tasks including semantic image segmentation. Since the current scenario is entirely unsupervised, the parameter estimation is performed iteratively using the Expectation Maximization (EM) technique considering the classes are distributed according to Gaussian functions. Further, we model the pairwise potential of the MRF cost function using a weighted combination of the similarity parameters. Results obtained on a fully polarimetric SAR data establishes the potential of such unsupervised random field models for analyzing SAR data effectively.
基于无监督隐马尔可夫随机场的极化SAR图像分割
提出了一种基于迭代无监督马尔可夫随机场(MRF)的极化合成孔径雷达(SAR)图像分割方法。为了完成包括语义图像分割在内的任务,MRF模型的参数估计通常是从可用的训练数据中进行的。由于当前场景是完全无监督的,考虑到类是根据高斯函数分布的,使用期望最大化(EM)技术迭代地进行参数估计。此外,我们使用相似性参数的加权组合对MRF成本函数的成对势进行建模。在完全极化SAR数据上获得的结果建立了这种无监督随机场模型有效分析SAR数据的潜力。
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