Unsupervised feature selection applied to SPOT5 satellite images indexing
Marine Campedel, Ivan Kyrgyzov, Henri Maitre;
JMLR W&P 4:48-59, 2008.
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.