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基于上下文分析的无监督分层迭代算法用于SAR图像分割

余航 焦李成 刘芳

余航, 焦李成, 刘芳. 基于上下文分析的无监督分层迭代算法用于SAR图像分割. 自动化学报, 2014, 40(1): 100-116. doi: 10.3724/SP.J.1004.2014.00100
引用本文: 余航, 焦李成, 刘芳. 基于上下文分析的无监督分层迭代算法用于SAR图像分割. 自动化学报, 2014, 40(1): 100-116. doi: 10.3724/SP.J.1004.2014.00100
YU Hang, JIAO Li-Cheng, LIU Fang. Context Based Unsupervised Hierarchical Iterative Algorithm for SAR Segmentation. ACTA AUTOMATICA SINICA, 2014, 40(1): 100-116. doi: 10.3724/SP.J.1004.2014.00100
Citation: YU Hang, JIAO Li-Cheng, LIU Fang. Context Based Unsupervised Hierarchical Iterative Algorithm for SAR Segmentation. ACTA AUTOMATICA SINICA, 2014, 40(1): 100-116. doi: 10.3724/SP.J.1004.2014.00100

基于上下文分析的无监督分层迭代算法用于SAR图像分割


DOI: 10.3724/SP.J.1004.2014.00100
详细信息
    作者简介:

    余航 西安电子科技大学博士研究生.2005 年获得西安电子科技大学学士学位. 主要研究方向为合成孔径雷达图像理解与解译,模式识别,计算机视觉. 本文通信作者.E-mail:yuhang9551@163.com

  • 基金项目:

    国家重点基础研究发展计划(973计划)(2013CB329402);国家自然科学基金(61072106,61173092,61271302,61272282,61001206,61202176,61271298);国家教育部博士点基金(20100203120005);教育部长江学者和创新团队支持计划(IRT1170)资助

Context Based Unsupervised Hierarchical Iterative Algorithm for SAR Segmentation

More Information
  • Fund Project:

    Supported by National Basic Research Program of China (973 Program) (2013CB329402), National Natural Science Foundation of China (61072106, 61173092, 61271302, 61272282, 61001206, 61202176, 61271298), National Research Foundation for the Doctoral Program of Higher Education of China (20100203120005), and the Program for Cheung Kong Scholars and Innovative Research Team in University (IRT1170)

  • 摘要: 基于聚类的分割算法能够有效地分析目标特征在特征域的分布结构,进而准确判断目标的所属类别,但难以利用图像的空间和边缘信息,而基于区域增长的分割算法能够在空间域利用多种图像信息计算目标之间的相似性,但缺乏对特征结构本身的深层挖掘,容易出现欠分割或过分割的结果. 本文结合这两种算法各自的优势,针对合成孔径雷达(Synthetic aperture radar,SAR)图像的特点,提出了一种基于上下文分析的无监督分层迭代算法. 该算法使用过分割区域作为操作单元,以提高分割速度,降低SAR图像相干斑噪声的影响. 在合并过分割区域时,该算法采用了分层迭代的策略:首先,设计了一种改进的模糊C均值聚类算法,对过分割区域的外观特征进行聚类分析,获得其类别标记,该类别标记包含了特征的分布结构信息. 然后,利用多种SAR图像特征对同类区域的空域上下文进行分析,使用区域迭代增长算法对全局范围内的相似区域进行合并,直到不存在满足合并条件的过分割区域对为止,再重新执行聚类算法. 这两种子算法分层交替迭代,扬长避短,实现了一种有效的方法来组织和利用多种信息对SAR图像进行分割. 对模拟和真实SAR图像的实验表明,本文提出的算法能够在区域一致性和细节保留之间做到很好的平衡,准确地分割出各类目标区域,对相干斑噪声具有很强的鲁棒性.
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基于上下文分析的无监督分层迭代算法用于SAR图像分割

doi: 10.3724/SP.J.1004.2014.00100
    基金项目:

    国家重点基础研究发展计划(973计划)(2013CB329402);国家自然科学基金(61072106,61173092,61271302,61272282,61001206,61202176,61271298);国家教育部博士点基金(20100203120005);教育部长江学者和创新团队支持计划(IRT1170)资助

    作者简介:

    余航 西安电子科技大学博士研究生.2005 年获得西安电子科技大学学士学位. 主要研究方向为合成孔径雷达图像理解与解译,模式识别,计算机视觉. 本文通信作者.E-mail:yuhang9551@163.com

摘要: 基于聚类的分割算法能够有效地分析目标特征在特征域的分布结构,进而准确判断目标的所属类别,但难以利用图像的空间和边缘信息,而基于区域增长的分割算法能够在空间域利用多种图像信息计算目标之间的相似性,但缺乏对特征结构本身的深层挖掘,容易出现欠分割或过分割的结果. 本文结合这两种算法各自的优势,针对合成孔径雷达(Synthetic aperture radar,SAR)图像的特点,提出了一种基于上下文分析的无监督分层迭代算法. 该算法使用过分割区域作为操作单元,以提高分割速度,降低SAR图像相干斑噪声的影响. 在合并过分割区域时,该算法采用了分层迭代的策略:首先,设计了一种改进的模糊C均值聚类算法,对过分割区域的外观特征进行聚类分析,获得其类别标记,该类别标记包含了特征的分布结构信息. 然后,利用多种SAR图像特征对同类区域的空域上下文进行分析,使用区域迭代增长算法对全局范围内的相似区域进行合并,直到不存在满足合并条件的过分割区域对为止,再重新执行聚类算法. 这两种子算法分层交替迭代,扬长避短,实现了一种有效的方法来组织和利用多种信息对SAR图像进行分割. 对模拟和真实SAR图像的实验表明,本文提出的算法能够在区域一致性和细节保留之间做到很好的平衡,准确地分割出各类目标区域,对相干斑噪声具有很强的鲁棒性.

English Abstract

余航, 焦李成, 刘芳. 基于上下文分析的无监督分层迭代算法用于SAR图像分割. 自动化学报, 2014, 40(1): 100-116. doi: 10.3724/SP.J.1004.2014.00100
引用本文: 余航, 焦李成, 刘芳. 基于上下文分析的无监督分层迭代算法用于SAR图像分割. 自动化学报, 2014, 40(1): 100-116. doi: 10.3724/SP.J.1004.2014.00100
YU Hang, JIAO Li-Cheng, LIU Fang. Context Based Unsupervised Hierarchical Iterative Algorithm for SAR Segmentation. ACTA AUTOMATICA SINICA, 2014, 40(1): 100-116. doi: 10.3724/SP.J.1004.2014.00100
Citation: YU Hang, JIAO Li-Cheng, LIU Fang. Context Based Unsupervised Hierarchical Iterative Algorithm for SAR Segmentation. ACTA AUTOMATICA SINICA, 2014, 40(1): 100-116. doi: 10.3724/SP.J.1004.2014.00100
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