  In this paper, we study the applicability of active learning in operative
scenarios: more particularly, we consider the well-known contradiction between
the active learning heuristics, which rank the pixels according to their
uncertainty, and the user's confidence in labeling, which is related to both
the homogeneity of the pixel context and user's knowledge of the scene. We
propose a filtering scheme based on a classifier that learns the confidence of
the user in labeling, thus minimizing the queries where the user would not be
able to provide a class for the pixel. The capacity of a model to learn the
user's confidence is studied in detail, also showing the effect of resolution
is such a learning task. Experiments on two QuickBird images of different
resolutions (with and without pansharpening) and considering committees of
users prove the efficiency of the filtering scheme proposed, which maximizes
the number of useful queries with respect to traditional active learning.
