贝叶斯框架下基于曲波特征加权的SAR影像分割
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桂林理工大学

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TP391

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广西科技计划项目(2020GXNSFBA297096;桂科AA18118038);国家自然科学基金项目(41901370);广西空间信息与测绘重点实验室基金项目(19-050-11-20)


SAR image segmentation based on weighted curvelet features in the Bayesian framework
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Guilin University of Technology

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    摘要:

    为了探究各特征在SAR影像分割中的作用规律,提出一种贝叶斯框架下基于曲波特征加权的SAR影像分割方法. 首先, 利用曲波变换提取像素的多尺度光谱特征, 构成像素特征矢量; 为了探究提取的多尺度光谱特征在SAR影像分割中的作用规律,赋予该矢量中的每个特征分量不同的贡献权重; 利用上述特征和贡献权重定义特征加权影像. 然后, 划分影像域,并在贝叶斯框架下构建基于曲波特征加权的SAR影像分割模型. 再利用马尔可夫链蒙特卡罗(Markov Chain Monte Carlo, MCMC)算法和最大期望值(Expectation Maximization, EM)算法实现影像分割和权重估计. 最后,利用提出方法和四种对比方法对SAR影像进行分割实验,通过其定性及定量评价结果验证提出方法不仅能自适应地确定特征在影像分割的作用, 还能有效提高SAR影像分割精度,以及提出方法在SAR影像多特征分割中的优势.

    Abstract:

    To research on the roles of multiple features in the SAR image segmentation, this paper presents a SAR image segmentation method with weighted curvelet feature in the Bayesian framework. Firstly, curvelet transform is used for SAR image to extract multiscale spectral features of every pixel, then pixels’ feature vectors can be formed. To research on the roles of extracted multiscale spectral features in the SAR image segmentation, a weight is assigned to a component in the pixel’s feature vector. Then, an image with feature weighted can be defined by the feature and weighs. And its image domain is partitioned. On the partitioned image domain, and a SAR image segmentation model based on weighted curvelet features is built in the Bayesian framework. Further, Markov Chain Monte Carlo (MCMC) and Expectation Maximization (EM) algorithms are used to segment image and estimate the weight values. Finally, the proposed method and four comparison methods are used to segment SAR image, the quantitative and qualitative results are illustrated that the proposed method can not only automatically determine the roles of multiple features in the segmentation procedure, but also improve the segmentation accuracy effectively, and the proposed method has strengths in the multi-feature segmentation of SAR image.

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  • 收稿日期:2020-12-15
  • 最后修改日期:2021-04-16
  • 录用日期:2021-04-21
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