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.