邹同元, 杨文, 代登信, 孙洪. 一种新的极化SAR图像非监督分类算法研究[J]. 武汉大学学报 ( 信息科学版), 2009, 34(8): 910-913.
引用本文: 邹同元, 杨文, 代登信, 孙洪. 一种新的极化SAR图像非监督分类算法研究[J]. 武汉大学学报 ( 信息科学版), 2009, 34(8): 910-913.
ZOU Tongyuan, YANG Wen, DAI Dengxin, SUN Hong. An Unsupervised Classification Method of POLSAR Image[J]. Geomatics and Information Science of Wuhan University, 2009, 34(8): 910-913.
Citation: ZOU Tongyuan, YANG Wen, DAI Dengxin, SUN Hong. An Unsupervised Classification Method of POLSAR Image[J]. Geomatics and Information Science of Wuhan University, 2009, 34(8): 910-913.

一种新的极化SAR图像非监督分类算法研究

An Unsupervised Classification Method of POLSAR Image

  • 摘要: 提出了一种建立在Mean-shift过分割结果区域图上的极化SAR图像非监督分类算法。首先通过Mean-shift算法得到极化SAR图像的过分割结果区域图,并将过分割小块视为“超级像素”,然后在Freeman-Durden分解的基础上引入散射功率熵和各向异性量参数来进一步分析“超级像素”的混合散射机制问题,最后结合Wishart迭代聚类实现极化SAR图像的非监督分类。实验表明,该算法具有较为满意的分类效果。

     

    Abstract: An unsupervised classification algorithm established on the mean-shift over-segmentation is presented in this paper. First,an over-segmentation result is obtained by a mean-shift algorithm and the segmentation patches are treated as "super-pixels". Then,based on Freeman-Durden decomposition,we survey the four different combinations of three basic scattering mechanisms by introducing two new parameters-the scattering power entropy and anisotropy. Finally,the iterative wishart classifier is applied to get the final classification results. The effectiveness of this algorithm is demonstrated by using the German aerospace center's (DLR) E-SAR L-band polarimetric synthetic aperture radar images.

     

/

返回文章
返回