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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >SVMeFC: SVM Ensemble Fuzzy Clustering for Satellite Image Segmentation
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SVMeFC: SVM Ensemble Fuzzy Clustering for Satellite Image Segmentation

机译：SVMeFC：用于卫星图像分割的SVM集成模糊聚类

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摘要

The problem of unsupervised image segmentation of a satellite image in a number of homogeneous regions can be viewed as the task of clustering the pixels in the intensity space. This letter presents an approach that exploits the capability of some recently proposed fuzzy clustering techniques, as well as support vector machine (SVM) classifiers, to yield improved solutions. All the fuzzy clustering techniques are first used to produce a set of different clustering solutions. Each such solution has been improved by a novel technique based on an SVM classifier. Thereafter, the cluster-based similarity partition algorithm is used to create the final clustering solution from all improved ensemble solutions. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing data described in terms of feature vectors. Moreover, a remotely sensed image of Calcutta City has been segmented using the proposed technique to establish its utility. In addition, the additional information of this letter is given as supplementary at http://sysbio.icm.edu.pl/indra/SVMeFC.html .
机译：可以将卫星图像在多个同质区域中的无监督图像分割问题视为在强度空间中对像素进行聚类的任务。这封信提出了一种方法，该方法利用了一些最近提出的模糊聚类技术以及支持向量机（SVM）分类器的功能，以产生改进的解决方案。首先使用所有模糊聚类技术来生成一组不同的聚类解决方案。每个此类解决方案都已通过基于SVM分类器的新颖技术得到了改进。此后，基于聚类的相似性分区算法用于从所有改进的集成解决方案中创建最终的聚类解决方案。为以特征向量描述的数字遥感数据提供了证明所提出技术有效性的结果。此外，使用所提出的技术对加尔各答市的遥感图像进行了分割，以建立其实用性。另外，这封信的其他信息在http://sysbio.icm.edu.pl/indra/SVMeFC.html上作为补充提供。

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