SAR图像双Markov-EAR模型的纹理无监督分割

单视SAR图像保留了最大的分辨率和场景可观测的全部纹理信息,根据单视SAR图像的统计性质,在双M arkov模型的框架下,对低层M arkov随机场提出了指数自回归EAR纹理模型,并对纹理含噪情形下的高层M arkov随机场模型给出了一种参数估计方法及相应的无监督分割算法。实验结果表明,与以往的有监督GAR模型和不考虑纹理的模型相比,无监督的双M arkov-EAR模型能大量降低分割时的错分率。

An Unsupervised and Higher-Precision Segmentation Method for Textured SAR Images Based on Pairwise Markov-EAR Model

Aim.The existing pairwise Markov-GAR(Gauss autoregressive) model for the segmentation of textured SAR(synthetic aperture radar) images suffers from two shortcomings :it requires supervision and its precision is not as good as can be.We now present what we believe to be a better segmentation method based on Markov-EAR(exponential autoregressive) model.In the full paper,we explain our Markov-EAR model in detail;in the abstract,we just add some pertinent remarks to listing the three topics of explanation:(1) pairwise Markov-EAR model;(2) parameter estimation based on pairwise Markov-EAR model;(3) the unsupervised segmentation method based on pairwise Markov-EAR model for textured SAR images;the two subtopics of topic 1 are Gibbs distribution based on labeling data(subtopic 1.1) and EAR model for SAR image data(subtopic 1.2);the two subtopics of topic 2 are parameter estimation based on LLMRF(low level Markov random field)(subtopic 2.1) and parameter estimation based on HLMRF(high level Markov random field)(subtopic 2.2);under subtopic 1.2,we derive eqs.(4),(5),and(7);under subtopic 2.1,we derive eqs.(9),(10),and(11);under subtopic 2.2,we derive eqs.(13),(14),and(15);the unsupervised segmentation algorithm given in topic 3 is directly based on the explanation given in topic 2.Finally we summarize our experimental results in Figs.2 and 3 and Table 1.The experimental results show preliminarily that :(1) the pixels incorrectly segmented are respectively 1665 and 2809 for the Markov-EAR model that does not require supervision and the Markov-GAR model that does;(2) the percentages of incorrect segmentation are respectively 2.5 and 4.4 for Markov-EAR model and Markov-GAR model.