An Effective Strategy to Reduce the Labeling Cost in the Definition of Training Sets by Active Learning

This letter proposes a novel strategy for reducing the cost of in situ sample labeling for the definition of training sets by active learning (AL) in the framework of supervised classification of remote sensing images. AL methods define a training set according to an iterative procedure that at each iteration requires the labeling of a set of new samples selected by the classifier. The proposed strategy can be embedded in any AL method to identify the most informative area on the ground where focusing each AL iteration to reduce the overall cost (in terms of time) of labeling. To this end, at each iteration, the most uncertain unlabeled samples are initially identified. Then, the area on the ground (having a size predefined by the user) that has the highest spatial density of informative (i.e., uncertain and diverse) unlabeled samples is selected by the proposed strategy, and the AL technique is applied only to the samples of that area. This results in a decrease of the overall labeling cost with respect to that required by the use of a given technique in a standard way. Experimental results obtained by embedding the presented strategy in different literature active learning methods confirm its effectiveness.