| 结合多元信息聚类与空间约束的遥感图像分割方法 |
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| 引用本文： | 李玉,徐秋晔,林文杰,赵泉华. 结合多元信息聚类与空间约束的遥感图像分割方法[J]. 信号处理, 2017, 33(5): 749-757. DOI: 10.16798/j.issn.1003-0530.2017.05.012 |
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| 作者姓名： | 李玉 徐秋晔 林文杰 赵泉华 |
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| 作者单位： | 辽宁工程技术大学测绘与地理科学学院遥感科学与应用研究所 |
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| 基金项目： | 国家自然科学基金青年基金(41301479)；辽宁省自然科学基金(2015020190)；国家自然科学基金面上项目(41271435)
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| 摘 要： | 为了解决传统聚类算法对聚类表征特征量的依赖性以及定义的不完备性，结合遥感图像的数据的空间位置关系提出了一种结合多元信息聚类与空间约束的遥感图像分割方法。针对某一聚类数据，以若干数据点(多元)组合的方式遍历其所有数据点，并定义多元组合的互信息，以表征该聚类的类内相似性；通过计算类外像素对类内多元组合的互信息，刻画类间的非相似性。在此基础上建立类内相似性和类间差异性，然后结合两者之间的平衡关系建立目标函数，并将Potts模型扩展到目标函数以加入空间约束，最后通过最大化目标函数实现图像分割。对模拟及真实全色遥感影像分割结果的定性、定量分析表明:结合多元信息聚类与空间约束的遥感影像分割方法可以避免聚类表征特征量的定义，从根本上消除其对图像分割的影响，并充分考虑遥感数据的空间位置关系。
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| 关 键 词： | 聚类算法 多元互信息 遥感图像分割 聚类特征量 |
| 收稿时间： | 2016-09-07 |
Remote Sensing Image Segmentation Method Combining Multivariate Information Clustering With Spatial Constraints |
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| Affiliation: | The Institute for Remote Sensing, School of Geomatics, Liaoning Technical University |
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| Abstract: | In order to deal with the problem that the traditional clustering methods were depended on clustering features and were difficult to find the features which can represent completely clusters, remote sensing image segmentation method combining multivariate information clustering with spatial constraints is proposed by considering the spatial position relation of remote sensing data. Given a cluster, several data points in the cluster are selected to form multi-tuple and all possible multi-tuples are employed as features to represent the cluster; And then mutual information of grayscales of pixels within each multi-tuple is calculated to characterize the similarity of the cluster; the dissimilarity between two clusters is defined by the mutual information of groups of pixels including multi-tuple of one cluster and pixels from another cluster. The objective function is formulated by balancing the above similarity and disdimularity. By maximizing the objective function, remote sensing image segmentation can be obtained. Comparing the segmentation results of simulated and real remote sensing image from qualitative and quantitative analyses indicate that the proposed algorithm can avoid the definition of clustering features, so it fundamentally eliminates its influence on image segmentation and considers adequately the spatial position relation. |
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