In this paper, we consider active sampling to label pixels grouped
with hierarchical clustering. The objective of the method is to match
the data relationships discovered by the clustering algorithm with
the user's desired class semantics. The first is represented as a
complete tree to be pruned and the second is iteratively provided
by the user. The active learning algorithm proposed searches the
pruning of the tree that best matches the labels of the sampled points.
By choosing the part of the tree to sample from according to current
pruning's uncertainty, sampling is focused on most uncertain clusters.
This way, large clusters for which the class membership is already
fixed are no longer queried and sampling is focused on division of
clusters showing mixed labels. The model is tested on a VHR image
in a multiclass classification setting. The method clearly outperforms
random sampling in a transductive setting, but cannot generalize
to unseen data, since it aims at optimizing the classification of
a given cluster structure.