| 条件随机场模型约束下的遥感影像模糊C-均值聚类算法 |
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| 引用本文： | 王少宇,焦洪赞,钟燕飞.条件随机场模型约束下的遥感影像模糊C-均值聚类算法[J].测绘学报,2016,45(12):1441-1447. |
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| 作者姓名： | 王少宇 焦洪赞 钟燕飞 |
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| 作者单位： | 1. 武汉大学遥感信息工程学院, 湖北 武汉 430079;2. 武汉大学城市设计学院, 湖北 武汉 430072;3. 武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉 430079 |
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| 基金项目： | 国家自然科学基金(41401400 |
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| 摘 要： | 遥感影像具有丰富的空间相关信息,而传统的基于像元光谱的聚类算法并不能将空间信息融入聚类,聚类结果往往不好。针对这一问题,本文提出了一种条件随机场模型约束下的模糊C-均值聚类算法,通过邻域像元的分类先验信息对中心像元的类别进行约束从而提取空间相关信息,基于二阶条件随机场将光谱信息和空间相关信息同时融入聚类,并使用环形置信度迭代算法得到像元分类后验概率的全局最优推测。试验证明,本文算法能够有效地保持地物的形状特征,分类精度相比传统算法有所提高。
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| 关 键 词： | 遥感影像聚类 模糊C-均值 条件随机场 空间相关信息 |
| 收稿时间： | 2015-12-11 |
| 修稿时间： | 2016-09-09 |
A Modified FCMClassifier Constrained by Conditional Random Field Model for Remote Sensi ng I magery |
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| WANG Shaoyu,JIAO Hongzan,ZHONG Yanfei.A Modified FCMClassifier Constrained by Conditional Random Field Model for Remote Sensi ng I magery[J].Acta Geodaetica et Cartographica Sinica,2016,45(12):1441-1447. |
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| Authors: | WANG Shaoyu JIAO Hongzan ZHONG Yanfei |
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| Institution: | 1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;2. School of Urban Design, Wuhan University, Wuhan 430072, China;3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China |
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| Abstract: | Remote sensing imagery has abundant spatial correlation information,but traditional pixel-based clustering algorithms don’t take the spatial information into account,therefore the results are often not good.To this issue,a modified FCM classifier constrained by conditional random field model is proposed.Adjacent pixels’priori classified information will have a constraint on the classification of the center pixel,thus extracting spatial correlation information.Spectral information and spatial correlation information are considered at the same time when clustering based on second order conditional random field.What’s more,the global optimal inference of pixel’s classified posterior probability can be get using loopy belief propagation.The experiment shows that the proposed algorithm can effectively maintain the shape feature of the object,and the classification accuracy is higher than traditional algorithms. |
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| Keywords: | clustering for remote sensing imagery fuzzy C-means conditional random field spatial correlation information |
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