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The high information redundancy in hyperspectral and hypertemporal Earth observation data can limit the performance of supervised learning algorithms. Traditional sequential feature selection approaches start the search on the full set of correlated features, which is a computationally expensive task and impedes the search and discovery of spectral or temporal segments relevant for classification...
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