Consensual clustering for unsupervised feature selection: application to SPOT5 satellite images indexing
Consensual clustering for unsupervised feature selection: application to SPOT5 satellite images indexing
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无监督特征选择的共识聚类：在 SPOT5 卫星图像索引中的应用
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
H. Maître
中科院分区：
文献类型：
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作者：
M. Campedel;Ivan O. Kyrgyzov;H. Maître
卫星图像数量众多，利用程度较低：迫切需要开发高效和快速的索引算法，以方便获取这些图像。为了确定要提取的最佳特征，我们提出了一种基于自动特征选择算法的方法，该方法无监督地应用于强冗余特征集。在这篇文章中，我们还展示了共识聚类作为一种特征选择算法的有效性，允许选择一些特征估计和探索设施。在SPOT5图像上验证了该方法的有效性。
Satellite images are numerous and weakly exploited: it is urgent to develop efficient and fast indexing algorithms to facilitate their access. In order to determinate the best features to be extracted, we propose a methodology based on automatic feature selection algorithms, applied unsupervisingly on a strongly redundant features set. In this article we also demonstrate the usefulness of consensus clustering as a feature selection algorithm, allowing selected number of features estimation and exploration facilities. The efficiency of our approach is demonstrated on SPOT5 images.