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一种无监督的遥感图像分割新算法研究 被引量：7

Study on a novel unsupervised algorithm for remote-sensing image segmentation
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摘要 由于遥感图像中存在边缘混叠和斑点噪声等问题,基于经典的马尔科夫随机场模型的分割算法效果并不太理想。本文针对遥感图像分割中某些像素分类的不确定性,将模糊MRF模型引入遥感图像分割领域,构造了基于MAP-FMRF的分割框架,提出了新的模糊MLL模型,在充分结合空间信息的同时利用灰度和纹理共同构造特征空间,以修正的EM算法结合SA算法获取全局最优解,实现无监督分割,实验对比证明该方法准确率更高。此外,文中还设计了一种新的优化方案以提高分割的效率。 Because remote-sensing applied, so the segmentation result image has interlaced edges and speckle noise and the standard MRF can not be of remote sense images using standard MRF is not satisfied. Aiming at the ambiguity in the segmentation of remote sense images, we apply the fuzzy Markov random field model to the segmentation of remote sense images. In this paper, a framework based on MAP-FMRF and a new fuzzy MLL model are proposed. We use gray and texture to construct the eigenspace with space information. Then we apply the EM algorithm and simulated annealing to search global optimal resolutions to realize the unsupervised image segmentation. Experiment comparison shows that our method is more efficient. In addition, we also devise an optimized scheme to improve the efficiency of segmentation.
作者 郑玮 康戈文 陈武凡 刘强
机构地区 金陵科技学院信息技术学院 电子科技大学
出处 《仪器仪表学报》 EI CAS CSCD 北大核心 2009年第1期213-218,共6页 Chinese Journal of Scientific Instrument
关键词 马尔可夫随机场模型 SAR图像分割 模糊MLL模型 SA算法 Markov random field model SAR image segmentation fuzzy MLL model SA algorithm
分类号 TP7 [自动化与计算机技术—检测技术与自动化装置]
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参考文献8

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共引文献201

同被引文献83

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引证文献7

二级引证文献31

仪器仪表学报
2009年 第1期

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