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图学学报

图学学报

• 专论：第十九届全国图象图形学学术会议(NCIG2018) • 上一篇    下一篇

基于边缘强度特征和线性谱聚类的 SAR 图像超像素生成方法

  

  1. 1. 国防科技大学电子科学学院，湖南 长沙 410073；
    2. 宇航动力学国家重点实验室，陕西 西安 710043
  • 出版日期:2018-12-31 发布日期:2019-02-20
  • 基金资助:
    国家自然科学基金项目(61701508)；湖南省自然科学基金项目(2017JJ2304)

Superpixel Generation Algorithm of SAR Image Based on Edge Strength Feature and Linear Spectral Clustering

  1. 1. College of Electronic Science, National University of Defense Technology, Changsha Hunan 410073, China;  
    2. State Key Laboratory of Astronautic Dynamics, Xi’an Shaanxi 710043, China
  • Online:2018-12-31 Published:2019-02-20

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摘要： 面对高分辨合成孔径雷达(SAR)图像的海量数据，学界广泛通过基于超像素的方法 简化图像处理过程。一般适用于光学图像的超像素分割算法对存在斑噪的 SAR 图像分割性能均 不够理想。面向 SAR 图像改进现有超像素生成算法是目前的研究热点之一。在探讨了将边缘强 度特征引入超像素分割算法的可行性的基础上，结合边缘强度特征和线性谱聚类方法，提出了 一种新的 SAR 图像超像素生成方法(e-LSC)。通过仿真 SAR 图像和实测 SAR 图像的比较实验， 证实了 e-LSC 算法与其他几种典型超像素生成算法相比，生成的超像素在边缘贴合度和匀质区 域的规则化上都有所提高。

关键词: 合成孔径雷达, 超像素, 线性谱聚类, 边缘强度特征

Abstract: In the face of massive data of high-resolution SAR images, the academic community widely simplifies image analysis processing through the superpixel-based approach. The superpixel segmentation algorithm which is generally suitable for optical images is not ideal for SAR images with speckle noise. Improving the existing superpixel generation algorithms for SAR image has been a hot topic among the scholars. In this paper, we discussed the feasibility of introducing edge strength feature into the superpixel segmentation algorithm. By combining the edge strength feature with the linear spectral clustering method, a novel superpixel generation algorithm (e-LSC) for SAR image was proposed. Compared with several typical superpixel generation algorithms on the simulated SAR image and the real SAR image, it is verified that the segmentation performance of e-LSC algorithm on the boundary adherence and the regularization of the homogeneous area is improved.

Key words:  synthetic aperture radar, superpixel, linear spectral cluster, edge strength feature