  Defining an efficient training set is one of the most delicate phases for the
success of remote sensing image classification routines. The complexity of the
problem, the limited temporal and financial resources, as well as the high
intraclass variance can make an algorithm fail if it is trained with a
suboptimal dataset. Active learning aims at building efficient training sets by
iteratively improving the model performance through sampling. A user-defined
heuristic ranks the unlabeled pixels according to a function of the uncertainty
of their class membership and then the user is asked to provide labels for the
most uncertain pixels. This paper reviews and tests the main families of active
learning algorithms: committee, large margin and posterior probability-based.
For each of them, the most recent advances in the remote sensing community are
discussed and some heuristics are detailed and tested. Several challenging
remote sensing scenarios are considered, including very high spatial resolution
and hyperspectral image classification. Finally, guidelines for choosing the
good architecture are provided for new and/or unexperienced user.
