基于GOF的SAR海冰图像无监督自动分类算法  

Automatic unsupervised classification of SAR images based on GOF MRF algorithm

作　　者：刘燕 石磊 Liu Yan;Shi Lei(School of Computer Science, Chengdu Normal University, Chengdu 611130,China)

机构地区：[1]成都师范学院计算机科学学院

出　　处：《电子测量与仪器学报》2019年第5期16-23,共8页Journal of Electronic Measurement and Instrumentation

基　　金：教育部产学研项目(201801187011);成都师范学院教改项目(2018JG25)资助

摘　　要：提出一种基于拟合优度(GOF)的合成孔径雷达(SAR)海冰图像无监督自动分类算法。该算法首先在高斯混合模型(GMM)参数进行估计过程中,附加了一个拟合优度(GOF)检验过程,在一定置信区间条件下,不仅能动态地选择最佳显著类别数,而且在马尔科夫随机场(MRF)计算最小能量对区域重新标记时,能提供初始特征参数;然后,再进行区域增长循环迭代;最后,得到各区域的最佳标记。实验结果证明,此算法在提高运行效率的同时,能够解决无监督图像分类过程中需要手动输入类别数的问题,而且输出结果图与专家解译的实况图相比在细节保持上效果更好,实用性更强。An unsupervised automatic classification algorithm for sea ice image of SAR based on goodness-of-fit test( GOF),which named GOF MRF is proposed. Firstly,in the process of estimating Gaussian mixture mode( GMM) parameters,a GOF process is added,which dynamically selects the best number of significant classes and provides an initial feature parameter to calculate the MRF minimum energy and remark the region under certain confidence intervals. Since the initial feature parameter selection of the MRF is not random,the operation efficiency is also improved while reducing the number of iteration cycles of the algorithm. The experiment results demonstrate that this algorithm solves the problem of manual input of the number of classes in the unsupervised image classification process,the output result graph is also better in terms of detail maintenance than the expert interpretation of the truth map,and can support the operation and meet the real-time requirements.

关 键 词：无监督分类 高斯混合模型 拟合优度检验 马尔科夫随机场 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TN919.8[自动化与计算机技术—计算机科学与技术]