Unsupervised feature selection applied to SPOT5 satellite images indexing
نویسندگان
چکیده
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.
منابع مشابه
Présentée pour obtenir le grade de docteur de l'École Nationale Supérieure des Télécommunications Spécialité: Signal et Images
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